Grad Coach

What (Exactly) Is Discourse Analysis? A Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Discourse analysis is one of the most popular qualitative analysis techniques we encounter at Grad Coach. If you’ve landed on this post, you’re probably interested in discourse analysis, but you’re not sure whether it’s the right fit for your project, or you don’t know where to start. If so, you’ve come to the right place.

Overview: Discourse Analysis Basics

In this post, we’ll explain in plain, straightforward language :

  • What discourse analysis is
  • When to use discourse analysis
  • The main approaches to discourse analysis
  • How to conduct discourse analysis

What is discourse analysis?

Let’s start with the word “discourse”.

In its simplest form, discourse is verbal or written communication between people that goes beyond a single sentence . Importantly, discourse is more than just language. The term “language” can include all forms of linguistic and symbolic units (even things such as road signs), and language studies can focus on the individual meanings of words. Discourse goes beyond this and looks at the overall meanings conveyed by language in context .  “Context” here refers to the social, cultural, political, and historical background of the discourse, and it is important to take this into account to understand underlying meanings expressed through language.

A popular way of viewing discourse is as language used in specific social contexts, and as such language serves as a means of prompting some form of social change or meeting some form of goal.

Discourse analysis goals

Now that we’ve defined discourse, let’s look at discourse analysis .

Discourse analysis uses the language presented in a corpus or body of data to draw meaning . This body of data could include a set of interviews or focus group discussion transcripts. While some forms of discourse analysis center in on the specifics of language (such as sounds or grammar), other forms focus on how this language is used to achieve its aims. We’ll dig deeper into these two above-mentioned approaches later.

As Wodak and Krzyżanowski (2008) put it: “discourse analysis provides a general framework to problem-oriented social research”. Basically, discourse analysis is used to conduct research on the use of language in context in a wide variety of social problems (i.e., issues in society that affect individuals negatively).

For example, discourse analysis could be used to assess how language is used to express differing viewpoints on financial inequality and would look at how the topic should or shouldn’t be addressed or resolved, and whether this so-called inequality is perceived as such by participants.

What makes discourse analysis unique is that it posits that social reality is socially constructed , or that our experience of the world is understood from a subjective standpoint. Discourse analysis goes beyond the literal meaning of words and languages

For example, people in countries that make use of a lot of censorship will likely have their knowledge, and thus views, limited by this, and will thus have a different subjective reality to those within countries with more lax laws on censorship.

social construction

When should you use discourse analysis?

There are many ways to analyze qualitative data (such as content analysis , narrative analysis , and thematic analysis ), so why should you choose discourse analysis? Well, as with all analysis methods, the nature of your research aims, objectives and research questions (i.e. the purpose of your research) will heavily influence the right choice of analysis method.

The purpose of discourse analysis is to investigate the functions of language (i.e., what language is used for) and how meaning is constructed in different contexts, which, to recap, include the social, cultural, political, and historical backgrounds of the discourse.

For example, if you were to study a politician’s speeches, you would need to situate these speeches in their context, which would involve looking at the politician’s background and views, the reasons for presenting the speech, the history or context of the audience, and the country’s social and political history (just to name a few – there are always multiple contextual factors).

The purpose of discourse analysis

Discourse analysis can also tell you a lot about power and power imbalances , including how this is developed and maintained, how this plays out in real life (for example, inequalities because of this power), and how language can be used to maintain it. For example, you could look at the way that someone with more power (for example, a CEO) speaks to someone with less power (for example, a lower-level employee).

Therefore, you may consider discourse analysis if you are researching:

  • Some form of power or inequality (for example, how affluent individuals interact with those who are less wealthy
  • How people communicate in a specific context (such as in a social situation with colleagues versus a board meeting)
  • Ideology and how ideas (such as values and beliefs) are shared using language (like in political speeches)
  • How communication is used to achieve social goals (such as maintaining a friendship or navigating conflict)

As you can see, discourse analysis can be a powerful tool for assessing social issues , as well as power and power imbalances . So, if your research aims and objectives are oriented around these types of issues, discourse analysis could be a good fit for you.

discourse analysis is good for analysing power

Discourse Analysis: The main approaches

There are two main approaches to discourse analysis. These are the language-in-use (also referred to as socially situated text and talk ) approaches and the socio-political approaches (most commonly Critical Discourse Analysis ). Let’s take a look at each of these.

Approach #1: Language-in-use

Language-in-use approaches focus on the finer details of language used within discourse, such as sentence structures (grammar) and phonology (sounds). This approach is very descriptive and is seldom seen outside of studies focusing on literature and/or linguistics.

Because of its formalist roots, language-in-use pays attention to different rules of communication, such as grammaticality (i.e., when something “sounds okay” to a native speaker of a language). Analyzing discourse through a language-in-use framework involves identifying key technicalities of language used in discourse and investigating how the features are used within a particular social context.

For example, English makes use of affixes (for example, “un” in “unbelievable”) and suffixes (“able” in “unbelievable”) but doesn’t typically make use of infixes (units that can be placed within other words to alter their meaning). However, an English speaker may say something along the lines of, “that’s un-flipping-believable”. From a language-in-use perspective, the infix “flipping” could be investigated by assessing how rare the phenomenon is in English, and then answering questions such as, “What role does the infix play?” or “What is the goal of using such an infix?”

Need a helping hand?

how to write a discourse analysis essay

Approach #2: Socio-political

Socio-political approaches to discourse analysis look beyond the technicalities of language and instead focus on the influence that language has in social context , and vice versa. One of the main socio-political approaches is Critical Discourse Analysis , which focuses on power structures (for example, the power dynamic between a teacher and a student) and how discourse is influenced by society and culture. Critical Discourse Analysis is born out of Michel Foucault’s early work on power, which focuses on power structures through the analysis of normalized power .

Normalized power is ingrained and relatively allusive. It’s what makes us exist within society (and within the underlying norms of society, as accepted in a specific social context) and do the things that we need to do. Contrasted to this, a more obvious form of power is repressive power , which is power that is actively asserted.

Sounds a bit fluffy? Let’s look at an example.

Consider a situation where a teacher threatens a student with detention if they don’t stop speaking in class. This would be an example of repressive power (i.e. it was actively asserted).

Normalized power, on the other hand, is what makes us not want to talk in class . It’s the subtle clues we’re given from our environment that tell us how to behave, and this form of power is so normal to us that we don’t even realize that our beliefs, desires, and decisions are being shaped by it.

In the view of Critical Discourse Analysis, language is power and, if we want to understand power dynamics and structures in society, we must look to language for answers. In other words, analyzing the use of language can help us understand the social context, especially the power dynamics.

words have power

While the above-mentioned approaches are the two most popular approaches to discourse analysis, other forms of analysis exist. For example, ethnography-based discourse analysis and multimodal analysis. Ethnography-based discourse analysis aims to gain an insider understanding of culture , customs, and habits through participant observation (i.e. directly observing participants, rather than focusing on pre-existing texts).

On the other hand, multimodal analysis focuses on a variety of texts that are both verbal and nonverbal (such as a combination of political speeches and written press releases). So, if you’re considering using discourse analysis, familiarize yourself with the various approaches available so that you can make a well-informed decision.

How to “do” discourse analysis

As every study is different, it’s challenging to outline exactly what steps need to be taken to complete your research. However, the following steps can be used as a guideline if you choose to adopt discourse analysis for your research.

Step 1: Decide on your discourse analysis approach

The first step of the process is to decide on which approach you will take in terms. For example, the language in use approach or a socio-political approach such as critical discourse analysis. To do this, you need to consider your research aims, objectives and research questions . Of course, this means that you need to have these components clearly defined. If you’re still a bit uncertain about these, check out our video post covering topic development here.

While discourse analysis can be exploratory (as in, used to find out about a topic that hasn’t really been touched on yet), it is still vital to have a set of clearly defined research questions to guide your analysis. Without these, you may find that you lack direction when you get to your analysis. Since discourse analysis places such a focus on context, it is also vital that your research questions are linked to studying language within context.

Based on your research aims, objectives and research questions, you need to assess which discourse analysis would best suit your needs. Importantly, you  need to adopt an approach that aligns with your study’s purpose . So, think carefully about what you are investigating and what you want to achieve, and then consider the various options available within discourse analysis.

It’s vital to determine your discourse analysis approach from the get-go , so that you don’t waste time randomly analyzing your data without any specific plan.

Action plan

Step 2: Design your collection method and gather your data

Once you’ve got determined your overarching approach, you can start looking at how to collect your data. Data in discourse analysis is drawn from different forms of “talk” and “text” , which means that it can consist of interviews , ethnographies, discussions, case studies, blog posts.  

The type of data you collect will largely depend on your research questions (and broader research aims and objectives). So, when you’re gathering your data, make sure that you keep in mind the “what”, “who” and “why” of your study, so that you don’t end up with a corpus full of irrelevant data. Discourse analysis can be very time-consuming, so you want to ensure that you’re not wasting time on information that doesn’t directly pertain to your research questions.

When considering potential collection methods, you should also consider the practicalities . What type of data can you access in reality? How many participants do you have access to and how much time do you have available to collect data and make sense of it? These are important factors, as you’ll run into problems if your chosen methods are impractical in light of your constraints.

Once you’ve determined your data collection method, you can get to work with the collection.

Collect your data

Step 3: Investigate the context

A key part of discourse analysis is context and understanding meaning in context. For this reason, it is vital that you thoroughly and systematically investigate the context of your discourse. Make sure that you can answer (at least the majority) of the following questions:

  • What is the discourse?
  • Why does the discourse exist? What is the purpose and what are the aims of the discourse?
  • When did the discourse take place?
  • Where did it happen?
  • Who participated in the discourse? Who created it and who consumed it?
  • What does the discourse say about society in general?
  • How is meaning being conveyed in the context of the discourse?

Make sure that you include all aspects of the discourse context in your analysis to eliminate any confounding factors. For example, are there any social, political, or historical reasons as to why the discourse would exist as it does? What other factors could contribute to the existence of the discourse? Discourse can be influenced by many factors, so it is vital that you take as many of them into account as possible.

Once you’ve investigated the context of your data, you’ll have a much better idea of what you’re working with, and you’ll be far more familiar with your content. It’s then time to begin your analysis.

Time to analyse

Step 4: Analyze your data

When performing a discourse analysis, you’ll need to look for themes and patterns .  To do this, you’ll start by looking at codes , which are specific topics within your data. You can find more information about the qualitative data coding process here.

Next, you’ll take these codes and identify themes. Themes are patterns of language (such as specific words or sentences) that pop up repeatedly in your data, and that can tell you something about the discourse. For example, if you’re wanting to know about women’s perspectives of living in a certain area, potential themes may be “safety” or “convenience”.

In discourse analysis, it is important to reach what is called data saturation . This refers to when you’ve investigated your topic and analyzed your data to the point where no new information can be found. To achieve this, you need to work your way through your data set multiple times, developing greater depth and insight each time. This can be quite time consuming and even a bit boring at times, but it’s essential.

Once you’ve reached the point of saturation, you should have an almost-complete analysis and you’re ready to move onto the next step – final review.

review your analysis

Step 5: Review your work

Hey, you’re nearly there. Good job! Now it’s time to review your work.

This final step requires you to return to your research questions and compile your answers to them, based on the analysis. Make sure that you can answer your research questions thoroughly, and also substantiate your responses with evidence from your data.

Usually, discourse analysis studies make use of appendices, which are referenced within your thesis or dissertation. This makes it easier for reviewers or markers to jump between your analysis (and findings) and your corpus (your evidence) so that it’s easier for them to assess your work.

When answering your research questions, make you should also revisit your research aims and objectives , and assess your answers against these. This process will help you zoom out a little and give you a bigger picture view. With your newfound insights from the analysis, you may find, for example, that it makes sense to expand the research question set a little to achieve a more comprehensive view of the topic.

Let’s recap…

In this article, we’ve covered quite a bit of ground. The key takeaways are:

  • Discourse analysis is a qualitative analysis method used to draw meaning from language in context.
  • You should consider using discourse analysis when you wish to analyze the functions and underlying meanings of language in context.
  • The two overarching approaches to discourse analysis are language-in-use and socio-political approaches .
  • The main steps involved in undertaking discourse analysis are deciding on your analysis approach (based on your research questions), choosing a data collection method, collecting your data, investigating the context of your data, analyzing your data, and reviewing your work.

If you have any questions about discourse analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your analysis, book an initial consultation with a friendly Grad Coach to see how we can help.

how to write a discourse analysis essay

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Thematic analysis explainer

30 Comments

Blessings sinkala

This was really helpful to me

Nancy Hatuyuni

I would like to know the importance of discourse analysis analysis to academic writing

Nehal Ahmad

In academic writing coherence and cohesion are very important. DA will assist us to decide cohesiveness of the continuum of discourse that are used in it. We can judge it well.

Sam

Thank you so much for this piece, can you please direct how I can use Discourse Analysis to investigate politics of ethnicity in a particular society

Donald David

Fantastically helpful! Could you write on how discourse analysis can be done using computer aided technique? Many thanks

Conrad

I would like to know if I can use discourse analysis to research on electoral integrity deviation and when election are considered free & fair

Robson sinzala Mweemba

I also to know the importance of discourse analysis and it’s purpose and characteristics

Tarien Human

Thanks, we are doing discourse analysis as a subject this year and this helped a lot!

ayoade olatokewa

Please can you help explain and answer this question? With illustrations,Hymes’ Acronym SPEAKING, as a feature of Discourse Analysis.

Devota Maria SABS

What are the three objectives of discourse analysis especially on the topic how people communicate between doctor and patient

David Marjot

Very useful Thank you for your work and information

omar

thank you so much , I wanna know more about discourse analysis tools , such as , latent analysis , active powers analysis, proof paths analysis, image analysis, rhetorical analysis, propositions analysis, and so on, I wish I can get references about it , thanks in advance

Asma Javed

Its beyond my expectations. It made me clear everything which I was struggling since last 4 months. 👏 👏 👏 👏

WAMBOI ELIZABETH

Thank you so much … It is clear and helpful

Khadija

Thanks for sharing this material. My question is related to the online newspaper articles on COVID -19 pandemic the way this new normal is constructed as a social reality. How discourse analysis is an appropriate approach to examine theese articles?

Tedros

This very helpful and interesting information

Mr Abi

This was incredible! And massively helpful.

I’m seeking further assistance if you don’t mind.

Just Me

Found it worth consuming!

Gloriamadu

What are the four types of discourse analysis?

mia

very helpful. And I’d like to know more about Ethnography-based discourse analysis as I’m studying arts and humanities, I’d like to know how can I use it in my study.

Rudy Galleher

Amazing info. Very happy to read this helpful piece of documentation. Thank you.

tilahun

is discourse analysis can take data from medias like TV, Radio…?

Mhmd ankaba

I need to know what is general discourse analysis

NASH

Direct to the point, simple and deep explanation. this is helpful indeed.

Nargiz

Thank you so much was really helpful

Suman Ghimire

really impressive

Maureen

Thank you very much, for the clear explanations and examples.

Ayesha

It is really awesome. Anybody within just in 5 minutes understand this critical topic so easily. Thank you so much.

Clara Chinyere Meierdierks

Thank you for enriching my knowledge on Discourse Analysis . Very helpful thanks again

Thuto Nnena

This was extremely helpful. I feel less anxious now. Thank you so much.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

  • Print Friendly

helpful professor logo

21 Great Examples of Discourse Analysis

discourse analysis example and definition, explained below

Discourse analysis is an approach to the study of language that demonstrates how language shapes reality. It usually takes the form of a textual or content analysis .

Discourse is understood as a way of perceiving, framing, and viewing the world.

For example:

  • A dominant discourse of gender often positions women as gentle and men as active heroes.
  • A dominant discourse of race often positions whiteness as the norm and colored bodies as ‘others’ (see: social construction of race )

Through discourse analysis, scholars look at texts and examine how those texts shape discourse.

In other words, it involves the examination of how the ‘ways of speaking about things’ normalizes and privileges some frames of thinking about things while marginalizing others.

As a simple example, if movies consistently frame the ideal female as passive, silent, and submissive, then society comes to think that this is how women should behave and makes us think that this is normal , so women who don’t fit this mold are abnormal .

Instead of seeing this as just the way things are, discourse analysts know that norms are produced in language and are not necessarily as natural as we may have assumed.

Examples of Discourse Analysis

1. language choice in policy texts.

A study of policy texts can reveal ideological frameworks and viewpoints of the writers of the policy. These sorts of studies often demonstrate how policy texts often categorize people in ways that construct social hierarchies and restrict people’s agency .

Examples include:

2. Newspaper Bias

Conducting a critical discourse analysis of newspapers involves gathering together a quorum of newspaper articles based on a pre-defined range and scope (e.g. newspapers from a particular set of publishers within a set date range).

Then, the researcher conducts a close examination of the texts to examine how they frame subjects (i.e. people, groups of people, etc.) from a particular ideological, political, or cultural perspective.

3. Language in Interviews

Discourse analysis can also be utilized to analyze interview transcripts. While coding methods to identify themes are the most common methods for analyzing interviews, discourse analysis is a valuable approach when looking at power relations and the framing of subjects through speech.

4. Television Analysis

Discourse analysis is commonly used to explore ideologies and framing devices in television shows and advertisements.

Due to the fact advertising is not just textual but rather multimodal , scholars often mix a discourse analytic methodology (i.e. exploring how television constructs dominant ways of thinking) with semiotic methods (i.e. exploration of how color, movement, font choice, and so on create meaning).

I did this, for example, in my PhD (listed below).

5. Film Critique

Scholars can explore discourse in film in a very similar way to how they study discourse in television shows. This can include the framing of sexuality gender, race, nationalism, and social class in films.

A common example is the study of Disney films and how they construct idealized feminine and masculine identities that children should aspire toward.

6. Analysis of Political Speech

Political speeches have also been subject to a significant amount of discourse analysis. These studies generally explore how influential politicians indicate a shift in policy and frame those policy shifts in the context of underlying ideological assumptions.

9. Examining Marketing Texts

Advertising is more present than ever in the context of neoliberal capitalism. As a result, it has an outsized role in shaping public discourse. Critical discourse analyses of advertising texts tend to explore how advertisements, and the capitalist context that underpins their proliferation, normalize gendered, racialized, and class-based discourses.

11. Analyzing Lesson Plans

As written texts, lesson plans can be analyzed for how they construct discourses around education as well as student and teacher identities. These texts tend to examine how teachers and governing bodies in education prioritize certain ideologies around what and how to learn. These texts can enter into discussions around the ‘history wars’ (what and whose history should be taught) as well as ideological approaches to religious and language learning.

12. Looking at Graffiti

One of my favorite creative uses of discourse analysis is in the study of graffiti. By looking at graffiti, researchers can identify how youth countercultures and counter discourses are spread through subversive means. These counterdiscourses offer ruptures where dominant discourses can be unsettled and displaced.

Get a Pdf of this article for class

Enjoy subscriber-only access to this article’s pdf

The Origins of Discourse Analysis

1. foucault.

French philosopher Michel Foucault is a central thinker who shaped discourse analysis. His work in studies like Madness and Civilization and The History of Sexuality demonstrate how our ideas about insanity and sexuality have been shaped through language.

The ways the church speaks about sex, for example, shapes people’s thoughts and feelings about it.

The church didn’t simply make sex a silent taboo. Rather, it actively worked to teach people that desire was a thing of evil, forcing them to suppress their desires.

Over time, society at large developed a suppressed normative approach to the concept of sex that is not necessarily normal except for the fact that the church reiterates that this is the only acceptable way of thinking about the topic.

Similarly, in Madness and Civilization , a discourse around insanity was examined. Medical discourse pathologized behaviors that were ‘abnormal’ as signs of insanity. Were the dominant medical discourse to change, it’s possible that abnormal people would no longer be seen as insane.

One clear example of this is homosexuality. Up until the 1990s, being gay was seen in medical discourse as an illness. Today, most of Western society sees that this way of looking at homosexuality was extremely damaging and exclusionary, and yet at the time, because it was the dominant discourse, people didn’t question it.

2. Norman Fairclough

Fairclough (2013), inspired by Foucault, created some key methodological frameworks for conducting discourse analysis.

Fairclough was one of the first scholars to articulate some frameworks around exploring ‘text as discourse’ and provided key tools for scholars to conduct analyses of newspaper and policy texts.

Today, most methodology chapters in dissertations that use discourse analysis will have extensive discussions of Fairclough’s methods.

Discourse analysis is a popular primary research method in media studies, cultural studies, education studies, and communication studies. It helps scholars to show how texts and language have the power to shape people’s perceptions of reality and, over time, shift dominant ways of framing thought. It also helps us to see how power flows thought texts, creating ‘in-groups’ and ‘out-groups’ in society.

Key examples of discourse analysis include the study of television, film, newspaper, advertising, political speeches, and interviews.

Al Kharusi, R. (2017). Ideologies of Arab media and politics: a CDA of Al Jazeera debates on the Yemeni revolution. PhD Dissertation: University of Hertfordshire.

Alaazi, D. A., Ahola, A. N., Okeke-Ihejirika, P., Yohani, S., Vallianatos, H., & Salami, B. (2021). Immigrants and the Western media: a CDA of newspaper framings of African immigrant parenting in Canada. Journal of Ethnic and Migration Studies , 47 (19), 4478-4496. Doi: https://doi.org/10.1080/1369183X.2020.1798746

Al-Khawaldeh, N. N., Khawaldeh, I., Bani-Khair, B., & Al-Khawaldeh, A. (2017). An exploration of graffiti on university’s walls: A corpus-based discourse analysis study. Indonesian Journal of Applied Linguistics , 7 (1), 29-42. Doi: https://doi.org/10.17509/ijal.v7i1.6856

Alsaraireh, M. Y., Singh, M. K. S., & Hajimia, H. (2020). Critical DA of gender representation of male and female characters in the animation movie, Frozen. Linguistica Antverpiensia , 104-121.

Baig, F. Z., Khan, K., & Aslam, M. J. (2021). Child Rearing and Gender Socialisation: A Feminist CDA of Kids’ Popular Fictional Movies. Journal of Educational Research and Social Sciences Review (JERSSR) , 1 (3), 36-46.

Barker, M. E. (2021). Exploring Canadian Integration through CDA of English Language Lesson Plans for Immigrant Learners. Canadian Journal of Applied Linguistics/Revue canadienne de linguistique appliquée , 24 (1), 75-91. Doi: https://doi.org/10.37213/cjal.2021.28959

Coleman, B. (2017). An Ideological Unveiling: Using Critical Narrative and Discourse Analysis to Examine Discursive White Teacher Identity. AERA Online Paper Repository .

Drew, C. (2013). Soak up the goodness: Discourses of Australian childhoods on television advertisements, 2006-2012. PhD Dissertation: Australian Catholic University. Doi: https://doi.org/10.4226/66/5a9780223babd

Fairclough, N. (2013). Critical discourse analysis: The critical study of language . London: Routledge.

Foucault, M. (1990). The history of sexuality: An introduction . London: Vintage.

Foucault, M. (2003). Madness and civilization . New York: Routledge.

Hahn, A. D. (2018). Uncovering the ideologies of internationalization in lesson plans through CDA. The New English Teacher , 12 (1), 121-121.

Isti’anah, A. (2018). Rohingya in media: CDA of Myanmar and Bangladesh newspaper headlines. Language in the Online and Offline World , 6 , 18-23. Doi: http://repository.usd.ac.id/id/eprint/25962

Khan, M. H., Adnan, H. M., Kaur, S., Qazalbash, F., & Ismail, I. N. (2020). A CDA of anti-Muslim rhetoric in Donald Trump’s historic 2016 AIPAC policy speech. Journal of Muslim Minority Affairs , 40 (4), 543-558. Doi: https://doi.org/10.1080/13602004.2020.1828507

Louise Cooper, K., Luck, L., Chang, E., & Dixon, K. (2021). What is the practice of spiritual care? A CDA of registered nurses’ understanding of spirituality. Nursing Inquiry , 28 (2), e12385. Doi: https://doi.org/10.1111/nin.12385

Mohammadi, D., Momeni, S., & Labafi, S. (2021). Representation of Iranians family’s life style in TV advertising (Case study: food ads). Religion & Communication , 27 (58), 333-379.

Munro, M. (2018) House price inflation in the news: a CDA of newspaper coverage in the UK. Housing Studies, 33(7), pp. 1085-1105. doi: 10.1080/02673037.2017.1421911

Ravn, I. M., Frederiksen, K., & Beedholm, K. (2016). The chronic responsibility: a CDA of Danish chronic care policies. Qualitative Health Research , 26 (4), 545-554. Doi: https://doi.org/10.1177%2F1049732315570133

Sengul, K. (2019). Critical discourse analysis in political communication research: a case study of right-wing populist discourse in Australia. Communication Research and Practice , 5 (4), 376-392. Doi: https://doi.org/10.1080/22041451.2019.1695082

Serafis, D., Kitis, E. D., & Archakis, A. (2018). Graffiti slogans and the construction of collective identity: evidence from the anti-austerity protests in Greece. Text & Talk , 38 (6), 775-797. Doi: https://doi.org/10.1515/text-2018-0023

Suphaborwornrat, W., & Punkasirikul, P. (2022). A Multimodal CDA of Online Soft Drink Advertisements. LEARN Journal: Language Education and Acquisition Research Network , 15 (1), 627-653.

Symes, C., & Drew, C. (2017). Education on the rails: a textual ethnography of university advertising in mobile contexts. Critical Studies in Education , 58 (2), 205-223. Doi: https://doi.org/10.1080/17508487.2016.1252783

Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education , 46 (2), 25-44. Doi: https://doi.org/10.1080/17508480509556423

Chris

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Self-Actualization Examples (Maslow's Hierarchy)
  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Forest Schools Philosophy & Curriculum, Explained!
  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Montessori's 4 Planes of Development, Explained!
  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Montessori vs Reggio Emilia vs Steiner-Waldorf vs Froebel

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

  • PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
  • EDIT Edit this Article
  • EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
  • Browse Articles
  • Learn Something New
  • Quizzes Hot
  • This Or That Game
  • Train Your Brain
  • Explore More
  • Support wikiHow
  • About wikiHow
  • Log in / Sign up
  • Education and Communications
  • College University and Postgraduate
  • Academic Writing

How to Do a Critical Discourse Analysis

Last Updated: April 7, 2023 Fact Checked

This article was co-authored by Christopher Taylor, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 91,697 times.

The field of critical discourse analysis (CDA) involves taking a deeper, qualitative look at different types of texts, whether in advertising, literature, or journalism. Analysts try to understand ways in which language connects to social, cultural, and political power structures. As understood by CDA, all forms of language and types of writing or imagery can convey and shape cultural norms and social traditions. While there is no single method that covers all types of critical discourse analyses, there are some grounding steps that you can take to ensure that your CDA is well done. [1] X Research source

Working with a Text

Step 1 Select a specific text that you'd like to analyze.

  • Texts could include things like Moby Dick , Citizen Kane , a cologne advertisement, a conversation between a doctor and their patient, or a piece of journalism describing an election.

Step 2 Look for words and phrases that reveal the text's attitude to its subject.

  • As a first step, circle all of the adverbs and adjectives in the text. Then, consider what they might suggest about the tone of the piece.
  • Look for tone words to help you figure out what the author is trying to convey.
  • For example, say you're looking at a piece of political journalism about the president. If the text describes the president as “the goofball in the Oval Office,” the attitude is sarcastic and critical.
  • However, if the president is described as “the leader of the free world,” the attitude is respectful and even reverential.
  • If the article simply refers to the president as “the president,” its attitude is deliberately neutral, as if the text refuses to “take sides.”

Step 3 Consider how the text includes or exclude readers from a community.

  • For example, think about a news report about international immigrants coming to a country. The newscaster can create different types of community by referring to the immigrants as “strangers,” “refugees,” or “aliens.”
  • The word “refugees” will prompt sympathy among listeners and will help build a community between citizens and immigrants, while “alien” will help create hostile feelings and will exclude the immigrants from the nation's community.

Step 4 Look for assumed interpretations that the text has already made.

  • For example, an 18th century short story that begins, “The savages attacked the unarmed settlers at dawn,” contains implicit interpretations and biases about indigenous populations.
  • Another story that begins, “The natives and settlers made a peaceful arrangement,” has a comparatively benign interpretation of historical events.

Analyzing the Text's Form and Production

Step 1 Think about the way your text has been produced.

  • For example, think about the difference between an author who writes a novel for money and one who writes for their own pleasure.
  • The first author would want to tap into popular trends ends of the day in order to profit, while the second author would be less concerned with pleasing the public.

Step 2 Examine the form of the text and consider who has access to it.

  • For example, consider the case of a CEO delivering a speech in person to their company. The fact that they're delivering a speech and not sending an open letter shows that openness and transparency are important to the CEO and the company culture.
  • If the CEO did not deliver a speech, but only sent an email to board members and top executives, the formal change would imply that the text had a very different audience. The email would make the CEO seem less personal, unconcerned about their own workers, and elitist in who they chose to address.

Step 3 Analyze quotations and borrowed language in your text.

  • For example, say that a contemporary writer opens a poem or story with: “It was the best of times, it was the worst of times.” Quoting Charles Dickens at once shows that the author is well-read and also grounds their writing in the English Victorian literary tradition.

Tracing Power in Social Practices

Step 1 Examine ways in which texts reveal traditions within a culture.

  • For example, if a political speakers says, “our forefathers smile upon us today,” they are using patriarchal language.
  • The term “culture” should be taken very broadly. Businesses can have cultures, as can communities of all sizes, countries, language groups, racial groups, and even hobbyists can have specific cultures.

Step 2 Contrast similar texts to find differences between the social cultures.

  • For example, consider 2 different magazine ads for trucks. In the first, a rugged-looking man sits in a truck below the words “The vehicle for men.” In the second, a family sits in a truck and the ad copy reads, “A truck to hold everybody.”
  • The first ad seems to rely on stereotypical ideas of masculinity, while the second seems more inclusive.

Step 3 Determine whether norms are held by a culture or a sub-culture.

  • For example, imagine a politician whose slogan is “All energy should come from coal!” Because of the extremity of the stance, you may suspect that the candidate represents a fringe party that doesn't share many of the mainstream party's views.
  • You could confirm this suspicion by looking at other candidates' speeches to see how they address the fringe candidate. If other candidates critique the fringe candidate, the latter is likely part of a sub-group whose views aren't shared by the main political culture.

Step 4 Consider ways in which cultural norms may exist internationally.

  • For example, companies like Ikea, Emirate Airlines, and McDonald's have strong cultures and norms that exist internationally.

Expert Q&A

  • In an academic setting, CDA isn't tied to 1 single field or discipline. Instead, CDA helps students in a variety of fields understand ways in which the production of texts carries cultural meaning. Thanks Helpful 1 Not Helpful 0
  • As with any other theoretical field, there are many different ways to perform critical discourse analyses. However, they're largely the same at the core: the models all examine ways in which texts at the smallest (word-based) and the largest (social and cultural) levels have an impact on how communities are formed and what readers believe about the world. Thanks Helpful 1 Not Helpful 0

how to write a discourse analysis essay

You Might Also Like

Write

  • ↑ https://www.history.ac.uk/1807commemorated/media/methods/critical.html
  • ↑ https://pages.gseis.ucla.edu/faculty/kellner/ed270/Luke/SAHA6.html#4
  • ↑ https://study.com/academy/lesson/interpreting-literary-meaning-how-to-use-text-to-guide-your-interpretation.html
  • ↑ https://www.scribbr.com/methodology/discourse-analysis/
  • ↑ https://youtu.be/3w_5riFCMGA?t=378
  • ↑ https://youtu.be/3w_5riFCMGA?t=669
  • ↑ https://www.uv.es/gimenez/Recursos/criticaldiscourse.pdf
  • ↑ https://youtu.be/3w_5riFCMGA?t=358

About This Article

Christopher Taylor, PhD

  • Send fan mail to authors

Reader Success Stories

Andrew Boyle

Andrew Boyle

Jul 6, 2020

Did this article help you?

Andrew Boyle

Oct 23, 2020

Aliyu Muhammad

Aliyu Muhammad

Nov 29, 2022

SK Chakraborty

SK Chakraborty

Apr 16, 2022

Chang Kim

Aug 2, 2019

Do I Have a Dirty Mind Quiz

Featured Articles

Does My Crush Like Me Quiz

Trending Articles

18 Practical Ways to Celebrate Pride as an Ally

Watch Articles

Clean Silver Jewelry with Vinegar

  • Terms of Use
  • Privacy Policy
  • Do Not Sell or Share My Info
  • Not Selling Info

Don’t miss out! Sign up for

wikiHow’s newsletter

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Methodology
  • Critical Discourse Analysis | Definition, Guide & Examples

Critical Discourse Analysis | Definition, Guide & Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Discourse analysis is a research method for studying written or spoken language in relation to its social context. It aims to understand how language is used in real-life situations.

When you do discourse analysis, you might focus on:

  • The purposes and effects of different types of language
  • Cultural rules and conventions in communication
  • How values, beliefs, and assumptions are communicated
  • How language use relates to its social, political, and historical context

Discourse analysis is a common qualitative research method in many humanities and social science disciplines, including linguistics, sociology, anthropology, psychology, and cultural studies. It is also called critical discourse analysis.

Table of contents

What is discourse analysis used for, how is discourse analysis different from other methods, how to conduct discourse analysis.

Conducting discourse analysis means examining how language functions and how meaning is created in different social contexts. It can be applied to any instance of written or oral language, as well as non-verbal aspects of communication, such as tone and gestures.

Materials that are suitable for discourse analysis include:

  • Books, newspapers, and periodicals
  • Marketing material, such as brochures and advertisements
  • Business and government documents
  • Websites, forums, social media posts, and comments
  • Interviews and conversations

By analysing these types of discourse, researchers aim to gain an understanding of social groups and how they communicate.

Prevent plagiarism, run a free check.

Unlike linguistic approaches that focus only on the rules of language use, discourse analysis emphasises the contextual meaning of language.

It focuses on the social aspects of communication and the ways people use language to achieve specific effects (e.g., to build trust, to create doubt, to evoke emotions, or to manage conflict).

Instead of focusing on smaller units of language, such as sounds, words, or phrases, discourse analysis is used to study larger chunks of language, such as entire conversations, texts, or collections of texts. The selected sources can be analysed on multiple levels.

Discourse analysis is a qualitative and interpretive method of analysing texts (in contrast to more systematic methods like content analysis ). You make interpretations based on both the details of the material itself and on contextual knowledge.

There are many different approaches and techniques you can use to conduct discourse analysis, but the steps below outline the basic structure you need to follow.

Step 1: Define the research question and select the content of analysis

To do discourse analysis, you begin with a clearly defined research question . Once you have developed your question, select a range of material that is appropriate to answer it.

Discourse analysis is a method that can be applied both to large volumes of material and to smaller samples, depending on the aims and timescale of your research.

Step 2: Gather information and theory on the context

Next, you must establish the social and historical context in which the material was produced and intended to be received. Gather factual details of when and where the content was created, who the author is, who published it, and whom it was disseminated to.

As well as understanding the real-life context of the discourse, you can also conduct a literature review on the topic and construct a theoretical framework to guide your analysis.

Step 3: Analyse the content for themes and patterns

This step involves closely examining various elements of the material – such as words, sentences, paragraphs, and overall structure – and relating them to attributes, themes, and patterns relevant to your research question.

Step 4: Review your results and draw conclusions

Once you have assigned particular attributes to elements of the material, reflect on your results to examine the function and meaning of the language used. Here, you will consider your analysis in relation to the broader context that you established earlier to draw conclusions that answer your research question.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Luo, A. (2022, December 05). Critical Discourse Analysis | Definition, Guide & Examples. Scribbr. Retrieved 3 June 2024, from https://www.scribbr.co.uk/research-methods/discourse-analysis-explained/

Is this article helpful?

Amy Luo

Other students also liked

Case study | definition, examples & methods, how to do thematic analysis | guide & examples, content analysis | a step-by-step guide with examples.

  • Privacy Policy

Research Method

Home » Discourse Analysis – Methods, Types and Examples

Discourse Analysis – Methods, Types and Examples

Table of Contents

Discourse Analysis

Discourse Analysis

Definition:

Discourse Analysis is a method of studying how people use language in different situations to understand what they really mean and what messages they are sending. It helps us understand how language is used to create social relationships and cultural norms.

It examines language use in various forms of communication such as spoken, written, visual or multi-modal texts, and focuses on how language is used to construct social meaning and relationships, and how it reflects and reinforces power dynamics, ideologies, and cultural norms.

Types of Discourse Analysis

Some of the most common types of discourse analysis are:

Conversation Analysis

This type of discourse analysis focuses on analyzing the structure of talk and how participants in a conversation make meaning through their interaction. It is often used to study face-to-face interactions, such as interviews or everyday conversations.

Critical discourse Analysis

This approach focuses on the ways in which language use reflects and reinforces power relations, social hierarchies, and ideologies. It is often used to analyze media texts or political speeches, with the aim of uncovering the hidden meanings and assumptions that are embedded in these texts.

Discursive Psychology

This type of discourse analysis focuses on the ways in which language use is related to psychological processes such as identity construction and attribution of motives. It is often used to study narratives or personal accounts, with the aim of understanding how individuals make sense of their experiences.

Multimodal Discourse Analysis

This approach focuses on analyzing not only language use, but also other modes of communication, such as images, gestures, and layout. It is often used to study digital or visual media, with the aim of understanding how different modes of communication work together to create meaning.

Corpus-based Discourse Analysis

This type of discourse analysis uses large collections of texts, or corpora, to analyze patterns of language use across different genres or contexts. It is often used to study language use in specific domains, such as academic writing or legal discourse.

Descriptive Discourse

This type of discourse analysis aims to describe the features and characteristics of language use, without making any value judgments or interpretations. It is often used in linguistic studies to describe grammatical structures or phonetic features of language.

Narrative Discourse

This approach focuses on analyzing the structure and content of stories or narratives, with the aim of understanding how they are constructed and how they shape our understanding of the world. It is often used to study personal narratives or cultural myths.

Expository Discourse

This type of discourse analysis is used to study texts that explain or describe a concept, process, or idea. It aims to understand how information is organized and presented in such texts and how it influences the reader’s understanding of the topic.

Argumentative Discourse

This approach focuses on analyzing texts that present an argument or attempt to persuade the reader or listener. It aims to understand how the argument is constructed, what strategies are used to persuade, and how the audience is likely to respond to the argument.

Discourse Analysis Conducting Guide

Here is a step-by-step guide for conducting discourse analysis:

  • What are you trying to understand about the language use in a particular context?
  • What are the key concepts or themes that you want to explore?
  • Select the data: Decide on the type of data that you will analyze, such as written texts, spoken conversations, or media content. Consider the source of the data, such as news articles, interviews, or social media posts, and how this might affect your analysis.
  • Transcribe or collect the data: If you are analyzing spoken language, you will need to transcribe the data into written form. If you are using written texts, make sure that you have access to the full text and that it is in a format that can be easily analyzed.
  • Read and re-read the data: Read through the data carefully, paying attention to key themes, patterns, and discursive features. Take notes on what stands out to you and make preliminary observations about the language use.
  • Develop a coding scheme : Develop a coding scheme that will allow you to categorize and organize different types of language use. This might include categories such as metaphors, narratives, or persuasive strategies, depending on your research question.
  • Code the data: Use your coding scheme to analyze the data, coding different sections of text or spoken language according to the categories that you have developed. This can be a time-consuming process, so consider using software tools to assist with coding and analysis.
  • Analyze the data: Once you have coded the data, analyze it to identify patterns and themes that emerge. Look for similarities and differences across different parts of the data, and consider how different categories of language use are related to your research question.
  • Interpret the findings: Draw conclusions from your analysis and interpret the findings in relation to your research question. Consider how the language use in your data sheds light on broader cultural or social issues, and what implications it might have for understanding language use in other contexts.
  • Write up the results: Write up your findings in a clear and concise way, using examples from the data to support your arguments. Consider how your research contributes to the broader field of discourse analysis and what implications it might have for future research.

Applications of Discourse Analysis

Here are some of the key areas where discourse analysis is commonly used:

  • Political discourse: Discourse analysis can be used to analyze political speeches, debates, and media coverage of political events. By examining the language used in these contexts, researchers can gain insight into the political ideologies, values, and agendas that underpin different political positions.
  • Media analysis: Discourse analysis is frequently used to analyze media content, including news reports, television shows, and social media posts. By examining the language used in media content, researchers can understand how media narratives are constructed and how they influence public opinion.
  • Education : Discourse analysis can be used to examine classroom discourse, student-teacher interactions, and educational policies. By analyzing the language used in these contexts, researchers can gain insight into the social and cultural factors that shape educational outcomes.
  • Healthcare : Discourse analysis is used in healthcare to examine the language used by healthcare professionals and patients in medical consultations. This can help to identify communication barriers, cultural differences, and other factors that may impact the quality of healthcare.
  • Marketing and advertising: Discourse analysis can be used to analyze marketing and advertising messages, including the language used in product descriptions, slogans, and commercials. By examining these messages, researchers can gain insight into the cultural values and beliefs that underpin consumer behavior.

When to use Discourse Analysis

Discourse analysis is a valuable research methodology that can be used in a variety of contexts. Here are some situations where discourse analysis may be particularly useful:

  • When studying language use in a particular context: Discourse analysis can be used to examine how language is used in a specific context, such as political speeches, media coverage, or healthcare interactions. By analyzing language use in these contexts, researchers can gain insight into the social and cultural factors that shape communication.
  • When exploring the meaning of language: Discourse analysis can be used to examine how language is used to construct meaning and shape social reality. This can be particularly useful in fields such as sociology, anthropology, and cultural studies.
  • When examining power relations: Discourse analysis can be used to examine how language is used to reinforce or challenge power relations in society. By analyzing language use in contexts such as political discourse, media coverage, or workplace interactions, researchers can gain insight into how power is negotiated and maintained.
  • When conducting qualitative research: Discourse analysis can be used as a qualitative research method, allowing researchers to explore complex social phenomena in depth. By analyzing language use in a particular context, researchers can gain rich and nuanced insights into the social and cultural factors that shape communication.

Examples of Discourse Analysis

Here are some examples of discourse analysis in action:

  • A study of media coverage of climate change: This study analyzed media coverage of climate change to examine how language was used to construct the issue. The researchers found that media coverage tended to frame climate change as a matter of scientific debate rather than a pressing environmental issue, thereby undermining public support for action on climate change.
  • A study of political speeches: This study analyzed political speeches to examine how language was used to construct political identity. The researchers found that politicians used language strategically to construct themselves as trustworthy and competent leaders, while painting their opponents as untrustworthy and incompetent.
  • A study of medical consultations: This study analyzed medical consultations to examine how language was used to negotiate power and authority between doctors and patients. The researchers found that doctors used language to assert their authority and control over medical decisions, while patients used language to negotiate their own preferences and concerns.
  • A study of workplace interactions: This study analyzed workplace interactions to examine how language was used to construct social identity and maintain power relations. The researchers found that language was used to construct a hierarchy of power and status within the workplace, with those in positions of authority using language to assert their dominance over subordinates.

Purpose of Discourse Analysis

The purpose of discourse analysis is to examine the ways in which language is used to construct social meaning, relationships, and power relations. By analyzing language use in a systematic and rigorous way, discourse analysis can provide valuable insights into the social and cultural factors that shape communication and interaction.

The specific purposes of discourse analysis may vary depending on the research context, but some common goals include:

  • To understand how language constructs social reality: Discourse analysis can help researchers understand how language is used to construct meaning and shape social reality. By analyzing language use in a particular context, researchers can gain insight into the cultural and social factors that shape communication.
  • To identify power relations: Discourse analysis can be used to examine how language use reinforces or challenges power relations in society. By analyzing language use in contexts such as political discourse, media coverage, or workplace interactions, researchers can gain insight into how power is negotiated and maintained.
  • To explore social and cultural norms: Discourse analysis can help researchers understand how social and cultural norms are constructed and maintained through language use. By analyzing language use in different contexts, researchers can gain insight into how social and cultural norms are reproduced and challenged.
  • To provide insights for social change: Discourse analysis can provide insights that can be used to promote social change. By identifying problematic language use or power imbalances, researchers can provide insights that can be used to challenge social norms and promote more equitable and inclusive communication.

Characteristics of Discourse Analysis

Here are some key characteristics of discourse analysis:

  • Focus on language use: Discourse analysis is centered on language use and how it constructs social meaning, relationships, and power relations.
  • Multidisciplinary approach: Discourse analysis draws on theories and methodologies from a range of disciplines, including linguistics, anthropology, sociology, and psychology.
  • Systematic and rigorous methodology: Discourse analysis employs a systematic and rigorous methodology, often involving transcription and coding of language data, in order to identify patterns and themes in language use.
  • Contextual analysis : Discourse analysis emphasizes the importance of context in shaping language use, and takes into account the social and cultural factors that shape communication.
  • Focus on power relations: Discourse analysis often examines power relations and how language use reinforces or challenges power imbalances in society.
  • Interpretive approach: Discourse analysis is an interpretive approach, meaning that it seeks to understand the meaning and significance of language use from the perspective of the participants in a particular discourse.
  • Emphasis on reflexivity: Discourse analysis emphasizes the importance of reflexivity, or self-awareness, in the research process. Researchers are encouraged to reflect on their own positionality and how it may shape their interpretation of language use.

Advantages of Discourse Analysis

Discourse analysis has several advantages as a methodological approach. Here are some of the main advantages:

  • Provides a detailed understanding of language use: Discourse analysis allows for a detailed and nuanced understanding of language use in specific social contexts. It enables researchers to identify patterns and themes in language use, and to understand how language constructs social reality.
  • Emphasizes the importance of context : Discourse analysis emphasizes the importance of context in shaping language use. By taking into account the social and cultural factors that shape communication, discourse analysis provides a more complete understanding of language use than other approaches.
  • Allows for an examination of power relations: Discourse analysis enables researchers to examine power relations and how language use reinforces or challenges power imbalances in society. By identifying problematic language use, discourse analysis can contribute to efforts to promote social justice and equality.
  • Provides insights for social change: Discourse analysis can provide insights that can be used to promote social change. By identifying problematic language use or power imbalances, researchers can provide insights that can be used to challenge social norms and promote more equitable and inclusive communication.
  • Multidisciplinary approach: Discourse analysis draws on theories and methodologies from a range of disciplines, including linguistics, anthropology, sociology, and psychology. This multidisciplinary approach allows for a more holistic understanding of language use in social contexts.

Limitations of Discourse Analysis

Some Limitations of Discourse Analysis are as follows:

  • Time-consuming and resource-intensive: Discourse analysis can be a time-consuming and resource-intensive process. Collecting and transcribing language data can be a time-consuming task, and analyzing the data requires careful attention to detail and a significant investment of time and resources.
  • Limited generalizability: Discourse analysis is often focused on a particular social context or community, and therefore the findings may not be easily generalized to other contexts or populations. This means that the insights gained from discourse analysis may have limited applicability beyond the specific context being studied.
  • Interpretive nature: Discourse analysis is an interpretive approach, meaning that it relies on the interpretation of the researcher to identify patterns and themes in language use. This subjectivity can be a limitation, as different researchers may interpret language data differently.
  • Limited quantitative analysis: Discourse analysis tends to focus on qualitative analysis of language data, which can limit the ability to draw statistical conclusions or make quantitative comparisons across different language uses or contexts.
  • Ethical considerations: Discourse analysis may involve the collection and analysis of sensitive language data, such as language related to trauma or marginalization. Researchers must carefully consider the ethical implications of collecting and analyzing this type of data, and ensure that the privacy and confidentiality of participants is protected.

About the author

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Documentary Analysis

Documentary Analysis – Methods, Applications and...

Histogram

Histogram – Types, Examples and Making Guide

Critical Analysis

Critical Analysis – Types, Examples and Writing...

Narrative Analysis

Narrative Analysis – Types, Methods and Examples

Textual Analysis

Textual Analysis – Types, Examples and Guide

Bimodal Histogram

Bimodal Histogram – Definition, Examples

  • How it works

researchprospect post subheader

Discourse Analysis – A Definitive Guide With Steps & Types

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

What is Discourse Analysis?

Discourse analysis is an essential aspect of studying a language and its uses in day-to-day life.

It aims to gain in-depth knowledge about the language and identify its association with society, culture, and people’s perception.

It is used in various social science and humanities disciplines, such as linguistic, sociolinguistics, and psycholinguistics.

Aims of Discourse Analysis

It focuses on

  • The clear, in-depth meaning of the language.
  • The uses of language and its effects.
  • The association of the language with cultures, interpersonal relationships, and communication.
  • Various components of the language like vocabulary, grammar, pronunciation, tone of voice, fonts, and written form.

Uses of Discourse Analysis

Discourse analysis is

  • Used to study the language and its applications in texts and contexts.
  • It focuses on the entire conversation and real text instead of constructed or artificial text.
  • It helps linguists to know the role of language in improving the understanding of people.
  • It enables teachers to learn many language strategies to teach students writing/speaking skills better.

Materials Used in Discourse Analysis

The material includes

Types of Discourse

What to analyse, does your research methodology have the following.

  • Great Research/Sources
  • Perfect Language
  • Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Does your Research Methodology Have the Following

How to Conduct Discourse Analysis?

While conducting discourse analysis, you need to focus on the following points.

  • Purpose of the writer
  • The context of the speech/passage
  • Type of the language used.
  • The organisation of the text

You need to interpret the meaning and context of the discourse based on the available material and resources. There are various methods to conduct discourse analysis, but we are discussing the most basic method below.

Step1: Develop a Research Question

Like any other research in discourse analysis, it’s essential to have a  research question  to proceed with your study.  After selecting your research question, you need to find out the relevant resources to find the answer to it. Discourse analysis can be applied to smaller or larger samples depending on your research’s aims and requirements.

Example : If you want to find out the impact of plagiarism on the credibility of the authors. You can examine the relevant materials available on the topic from the internet, newspapers, and books published during the past 5-10 years.

Step 2: Collect Information and Establish the Context

After formulating a research question, you can  review the literature and find out the details about the source material, such as:

  • Who is the author?
  • What is the year and date of publication?
  • What’s the name of the publication?
  • What country and place is it from?
  • What language is used?
  • How and where did you find it?
  • How can others get access to the same source?
  • What kind of impact did it make on its audience?
  • What’s the association between discourse material and real life?

These questions enable you to construct a strong evidence-based theory about your study.

Example: While investigating the history and origin of a particular religion. You also have to research the political events, culture, language of the people, and their association with society.

Generally, details about the publication and production of the material are available in the  about section on their online websites. If you don’t find the relevant information online, don’t hesitate to contact the editor or publication via email, phone calls, etc. 

Step 3: Analyse the Content

In this step, you should analyse various aspects of the materials such as:

  • Sentence structure
  • Inter-relationship between the text
  • Layout and Page quality (if you are using offline materials)
  • Links, comments, technical excellence, readability, multimedia content (if you are using online material)
  • The genre of the source (a news item, political speech, a report, interview, biography, commentary, etc.)

The analysis of these elements gives you a clear understanding, and you can present your findings more accurately.  Once you have analysed the above features, you should analyse the following aspects:

  • The structure of the argument
  • The role of the introduction and conclusion of the material
  • The context of the material
  • Patterns and themes
  • Discursive statements (arguments, perspective, thoughts of the writer/speaker
  • Grammatical features (use of pronouns, adjectives, phrases, active or passive voice, and their meaning)
  • Literary figures (idioms, similes, metaphors, allegories, proverbs)

Step 4: Interpret the Data

Now you have all the information, but the question that arises here is: 

What does it all mean?

To answer this question,  compile all your findings  to explain the meaning and context of the discourse.

Step 5: Present your Findings

It’s time to present your results. Throughout the process, you gathered detailed notes of the discourse, building a strong presentation or thesis. You can use the references of other relevant sources as evidence to support your discussion. Always try to make your paper interesting to grab the attention of the reader.

Advantages and Disadvantages of Discourse Analysis

  • It provides a way of thinking and analysing the problem.
  • It enables us to understand the context and perception of the speaker.
  • It can be applied at any given time, place, and people.
  • It helps to learn any language its origin and association with society and culture.

Disadvantages

  • There are many options available as each tradition has its own concepts, procedures, and a specific understanding of discourse and its analysis.
  • Discourse analysis doesn’t help to find out the answer to scientific problems.

Frequently Asked Questions

How to describe the discourse analysis.

Discourse analysis examines language use in context. It studies how communication shapes and reflects social meaning, power dynamics, and cultural norms. By analyzing spoken, written, or visual language, it unveils hidden ideologies, identities, and social structures within various contexts.

You May Also Like

A hypothesis is a research question that has to be proved correct or incorrect through hypothesis testing – a scientific approach to test a hypothesis.

Thematic analysis is commonly used for qualitative data. Researchers give preference to thematic analysis when analysing audio or video transcripts.

Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers.

USEFUL LINKS

LEARNING RESOURCES

researchprospect-reviews-trust-site

COMPANY DETAILS

Research-Prospect-Writing-Service

  • How It Works

how to write a discourse analysis essay

The Plagiarism Checker Online For Your Academic Work

Start Plagiarism Check

Editing & Proofreading for Your Research Paper

Get it proofread now

Online Printing & Binding with Free Express Delivery

Configure binding now

  • Academic essay overview
  • The writing process
  • Structuring academic essays
  • Types of academic essays
  • Academic writing overview
  • Sentence structure
  • Academic writing process
  • Improving your academic writing
  • Titles and headings
  • APA style overview
  • APA citation & referencing
  • APA structure & sections
  • Citation & referencing
  • Structure and sections
  • APA examples overview
  • Commonly used citations
  • Other examples
  • British English vs. American English
  • Chicago style overview
  • Chicago citation & referencing
  • Chicago structure & sections
  • Chicago style examples
  • Citing sources overview
  • Citation format
  • Citation examples
  • College essay overview
  • Application
  • How to write a college essay
  • Types of college essays
  • Commonly confused words
  • Definitions
  • Dissertation overview
  • Dissertation structure & sections
  • Dissertation writing process
  • Graduate school overview
  • Application & admission
  • Study abroad
  • Master degree
  • Harvard referencing overview
  • Language rules overview
  • Grammatical rules & structures
  • Parts of speech
  • Punctuation
  • Methodology overview
  • Analyzing data
  • Experiments
  • Observations
  • Inductive vs. Deductive
  • Qualitative vs. Quantitative
  • Types of validity
  • Types of reliability
  • Sampling methods
  • Theories & Concepts
  • Types of research studies
  • Types of variables
  • MLA style overview
  • MLA examples
  • MLA citation & referencing
  • MLA structure & sections
  • Plagiarism overview
  • Plagiarism checker
  • Types of plagiarism
  • Printing production overview
  • Research bias overview
  • Types of research bias
  • Example sections
  • Types of research papers
  • Research process overview
  • Problem statement
  • Research proposal
  • Research topic
  • Statistics overview
  • Levels of measurment
  • Frequency distribution
  • Measures of central tendency
  • Measures of variability
  • Hypothesis testing
  • Parameters & test statistics
  • Types of distributions
  • Correlation
  • Effect size
  • Hypothesis testing assumptions
  • Types of ANOVAs
  • Types of chi-square
  • Statistical data
  • Statistical models
  • Spelling mistakes
  • Tips overview
  • Academic writing tips
  • Dissertation tips
  • Sources tips
  • Working with sources overview
  • Evaluating sources
  • Finding sources
  • Including sources
  • Types of sources

Your Step to Success

Plagiarism Check within 10min

Printing & Binding with 3D Live Preview

Discourse Analysis – Definition & How to Do It

How do you like this article cancel reply.

Save my name, email, and website in this browser for the next time I comment.

Discourse-Analysis-01

Discourse analysis utilizes a unique methodology designed to reveal the underlying significance of both written and spoken language. This methodology is often a focal point of study in higher education courses related to humanities, linguistics, or social sciences.

In this piece, we will delve into the specific applications and nuances of discourse analysis, providing a detailed, step-by-step guide to assist you in incorporating this methodology into your scholarly work.

Inhaltsverzeichnis

  • 1 Discourse Analysis – In a Nutshell
  • 2 Definition: Discourse analysis
  • 3 What is discourse analysis used for?
  • 4 Discourse analysis vs. other methods
  • 5 Discourse analysis: Step-by-step
  • 6 Discourse analysis: Advantages vs. disadvantages

Discourse Analysis – In a Nutshell

  • Discourse analysis can reveal deep motivations and meanings behind written and spoken language.
  • This technique is useful to students taking humanities, linguistics, or social sciences courses.
  • Learning to use discourse analysis can enrich your academic work.

Definition: Discourse analysis

Discourse analysis, which is sometimes abbreviated as DA, is a set of research methodologies created to uncover deep layers of meaning in different forms of speech, whether they are written or spoken.

As a research method, discourse analysis does not simply analyze language. Instead, it’s a tool that can reveal how language is used to express meaning and/or to achieve specific communicative goals.

You can apply different methods and perspectives to discourse analysis.

Discourse can be analyzed by taking into account the premises and assumptions of critical studies, anthropology, applied linguistics, sociology, translation studies, communication science, and psychology.

What is discourse analysis used for?

In academia, discourse analysis plays an important role in helping reveal nuances that can be very valuable in qualitative research . As such, it is commonly used by students of history, politics, sociology, linguistics, or gender studies to analyze past or current examples of discourse and to draw conclusions about the links between language and society.

As a student, you would want to use discourse analysis methodologies to reach a deeper level of analysis that can have a positive impact on your grades.

Ireland

Discourse analysis vs. other methods

Discourse analysis is not the only methodology that studies language. However, it substantially differs from other methods, like grammar analysis. While the latter is concerned with grammatical or syntactical structure, discourse analysis helps the researcher or student dig deep under such structures to find meaningful insights.

Another difference is that language-focused analysis techniques tend to study language components in isolation, whereas discourse analysis takes those elements and evaluates them considering the context in which they happen.

In addition, discourse analysis examines authentic forms of language as they occur in real life, while researchers or students using other methods are more likely to create their own samples and examples.

Discourse analysis: Step-by-step

Discourse-Analysis-step-by-step

1. Define your primary questions

If you’re using discourse analysis as a research tool, you’ll want to frame your research with one or two relevant research questions. This will help you stay on topic and bring coherence to your work.

2. Choose your analytical approach

Next, you want to choose an analytical approach that will help shape and guide your discourse assessment. Which approach you choose will depend on your course and degree subject. For example, if you’re studying anthropology, you could choose to interpret your discourse analysis findings based on postmodernist theory. Or if you’re studying media and communication, you could choose a semiotic approach.

3. Collect your data

This is where you gather your research materials, which can be written texts, conversation transcripts, videos, speeches, debates, etc.

4. Define the context

Be as specific as you can about the context in which the discourse takes place. Here you can consider social, political, historical, or geographical data. Then, you can start making hypotheses as to how context influences discourse, and vice versa.

5. Code your data

Coding means systematically tagging research data, based on meaningful categories. For example, if you were analyzing a political speech, you could create various data categories based on the themes that keep appearing throughout the speech (e.g. democracy, community, identity), then you would find all statements relevant to each theme.

Also, make sure the themes are related to your research question/s.

6. Look for patterns

Go over your coded materials and try to find recurring patterns. Are certain words, sentences, or ideas repeated? If you’re analyzing conversations, does one person dominate the interaction? Are there silences or pauses?

7. Analyze language use

Here, you go into detail about the various aspects of language use, such as metaphors, jargon, use of active and passive voice, use of persuasive statements, etc.

8. Interpret your findings

Keeping your research data and analytical framework in mind, try to uncover the meaning of the discourse you’re analyzing, always relative to your research question/s. Make sure you present evidence in support of your interpretation.

9. Summarize your findings

You can close a discourse analysis exercise with a summary of your findings and suggest areas for potential future research.

Discourse analysis: Advantages vs. disadvantages

What are the types of discourse analysis.

In academic settings, there are four main types of discourse analysis:

  • Focuses on analyzing how language is used to describe the characteristics of people, objects, concepts, or events.
  • Attempts to uncover the underlying story behind a text, speech, or communicative interaction.
  • Explores how language is used to tilt the audience in favor or against a topic or issue.
  • Examines language-in-use and how it conveys information.

Does discourse analysis only study language?

Not exclusively. In some cases, discourse analysis methodologies analyze non-verbal factors (such as body language or intonation) in order to reveal the rich meaning behind communicative acts.

What are the three most important factors in discourse analysis?

The context in which discourse takes place, as well as the patterns and themes that emerge from language use.

Does discourse analysis have applications outside academia?

Yes, it is also used by political analysts, in social policy, and in marketing research.

We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience.

  • External Media

Individual Privacy Preferences

Cookie Details Privacy Policy Imprint

Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.

Accept all Save

Essential cookies enable basic functions and are necessary for the proper function of the website.

Show Cookie Information Hide Cookie Information

Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website.

Content from video platforms and social media platforms is blocked by default. If External Media cookies are accepted, access to those contents no longer requires manual consent.

Privacy Policy Imprint

Welcome to PoliticsEastAsia: a network for East Asia scholars and enthusiasts

how to write a discourse analysis essay

How to Do a Discourse Analysis

A toolbox for analysing political texts.

Discourse analysis is a useful tool for studying the political meanings that inform written and spoken text. In other posts, I have provided a quick video introduction to the topic, and have discussed the ideas behind discourse theory , the main questions that students and researchers will likely ask as they set up their discourse analysis project , and the things that are worth keeping in mind when working with East Asian language sources . In this post, I offer a handy set of tools for doing a text-based, qualitative discourse analysis. The idea of a discourse toolbox comes from Siegfried Jäger, but I have expanded his approach based on my own experience and the works of other discourse analysts such as Paul Chilton (2004) and Norman Fairclough (1994).

You can go through the whole list of work-steps and tick each item off in turn, which is a good way to practice these methods. However, if you are conducting a specific research project, I would recommend adapting this toolbox to your own needs and tailoring it to fit your concerns. At the end of this post, you will also find a few comments on the limitations of this toolbox plus a list of literature that you can turn to if you want to learn more.

Getting technical: discourse analysis in ten steps

So you have formulated a research question, have collected source material, and are now ready to roll up your sleeves and dig into your sources. But how do you make sure that you have covered all your bases and that you will later be able to make a good case for yourself and your work? Here are ten work steps that will help you conduct a systematic and professional discourse analysis.

1) Establish the context

Before you start chiselling away at your source material, jot down where the material comes from and how it fits into the big picture . You should ask yourself what the social and historical context is in which each of your sources was produced. Write down what language your source is written in, what country and place it is from, who wrote it (and when), and who published it (and when). Also try to have a record of when and how you got your hands on your sources, and to explain where others might find copies. Finally, find out whether your sources are responses to any major event , whether they tie into broader debates , and how they were received at the time of publication.

2) Explore the production process

You have already recorded who wrote and published your sources, but you still need to do a more thorough background check . Try to find additional information on the producer of your source material, as well as their institutional and personal background. For example, if you are analysing news articles, take a look at the kind of newspaper that the articles are from (Jäger 2004: 175): Who are the author and the editorial staff, what is the general political position of the paper, and what is its affiliation with other organizations? Are any of the people who are involved in the production process known for their journalistic style or their political views? Is there any information on the production expenditures and general finances of the paper? Do you know who the general target audience of the paper is? In many cases, media outlets themselves provide some of this information online, for instance in the “about” sections of their websites. In other cases, you will find such information in the secondary academic literature. Don’t hesitate to write the editors an email or call them up: personal interviews can be a great way to explore production backgrounds.

Once you have established the institutional background, take notes on the medium and the genre you are working with. Some scholars go as far to argue that “the medium is the message” (McLuhan 1964/2001), or in other words that the medium in which information is presented is the crucial element that shapes meaning. While I am skeptical of such extreme technological determinism, I do agree that the medium matters : reading an article online is not the same as reading it in a printed newspaper, or in a hardcover collection of essays. Make sure to identify the different media types in which your source appeared, and to also be clear about the version that you yourself are analysing.

For instance, the layout of a newspaper article and its position on the page will be different in a print edition than in an online edition. The latter will also offer comments, links, multi-media content, etc. All of these factors frame the meaning of the actual text and should be considered in an analysis. This may also mean that you should think about the technical quality and readability of your source, for instance by looking at paper quality (or resolution for online sources), type set, etc. You should also take notes on the length of your source (number of pages and/or words) and any additional features of the medium that might contribute to or shape meaning (such as images).

Finally, ask yourself what genre your source belongs to. Are you analysing an editorial comment, and op-ed, a reader’s letter, a commentary, a news item, a report, an interview, or something else? Establishing this background information will later help you assess what genre-specific mechanism your source deploys (or ignores) to get its message across.

3) Prepare your material for analysis

In order to analyse the actual text, it is wise to prepare it in a way that will allow you to work with the source, home in on specific details, and make precise references later. If you are working with a hard copy I would recommend making a number of additional copies of your source material, so that you can write on these versions and mark important features . If you haven’t already, try to digitize your source or get a digital copy. Then add references that others can use to follow your work later: add numbers for lines, headers, paragraphs, figures, or any other features that will help you keep your bearings.

4) Code your material

When you code data, it means that you are assigning attributes to specific units of analysis, such as paragraphs, sentences, or individual words. Think of how many of us tag online information like pictures, links, or articles. Coding is simply an academic version of this tagging process.

For instance, you might be analysing a presidential speech to see what globalization discourse it draws from. It makes sense to mark all statements in the speech that deal with globalization and its related themes (or discourse strands ). Before you start with this process, you need to come up with your coding categories . The first step is to outline a few such categories theoretically: based on the kind of question you are asking, and your knowledge of the subject matter, you will already have a few key themes in mind that you expect to find, for instance “trade”, “migration”, “transportation”, “communication”, and so on. A thorough review of the secondary literature on your topic will likely offer inspiration. Write down your first considerations, and also write down topics that you think might be related to these key themes. These are your starting categories.

You then go over the text to see if it contains any of these themes. Take notes on the ones that are not included, since you may have to delete these categories later. Other categories might be too broad, so try breaking them down into sub-categories. Also, the text may include interesting themes that you did not expect to find, so jot down any such additional discourse strands. At the end of this first review, revise your list of coding categories to reflect your findings. If you are working with several documents, repeat the process for each of them, until you have your final list of coding categories. This is what Mayring (2002: 120) calls  evolutionary coding , since your categories evolve from theoretical considerations into a full-fledged operational list based on empirical data .

How the actual coding process works will depend on the tools you use. You can code paper-based sources by highlighting text sections in different colours, or by jotting down specific symbols. If you are working with a computer, you can similarly highlight text sections in a word processor. In either case, the risk is that you will not be able to represent multiple categories adequately, for instance when a statement ties into three or four discourse strands at once. You could mark individual words, but this might not be ideal if you want to see how the discourse works within the larger sentence structure, and how discourse strands overlap.

A real alternative is using other types of software. If you have access to professional research programmes like NVivo , then the software already has built-in coding mechanisms that you can customize and use. There is also open-source software available, for instance the Mac programme TAMS , but I have not tested their functionality. However, even if you only have regular office tools at your disposal, such as Microsoft’s Office or a Mac equivalent, there are at least two ways in which you can code material.

The first is to copy your text into an Excel table. Place the text in one column and use the next column to add the coding categories. You’ll of course have to decide where the line-breaks should be. A sensible approach is to place each sentence of your original text on a new line, but you could also choose smaller units of text.

Another tool that provides coding assistance is Microsoft OneNote 2010, or the Mac equivalent Growly Notes . In OneNote , you can right click anywhere in the text and select “tag” to assign a category to any sentence. You can also customize your tags, create new ones, and easily search and monitor your coding categories and activities. The downside is that you can only tag full sentences, not single words or phrases, but depending on your intentions, this may not be a crucial drawback.

5) Examine the structure of the text

Now that you have prepared your materials and have coded the discourse strands, it is time to look at the structural features of the texts. Are there sections that overwhelmingly deal with one discourse? Are there ways in which different discourse strands overlap in the text? See if you can identify how the argument is structured: does the text go through several issues one by one? Does it first make a counter-factual case, only to then refute that case and make the main argument? You should at this point also consider how the headers and other layout features guide the argument, and what role the introduction and conclusion play in the overall scheme of things.

6) Collect and examine discursive statements

Once you have a good idea of the macro-features of your text, you can zoom in on the individual statements, or discourse fragments . A good way to do this is to collect all statements with a specific code, and to examine what they have to say on the respective discourse strand. This collection of statements will allow you to map out what “truths” the text establishes on each major topic.

7) Identify cultural references

You have already established what the context of your source material is. Now think about how the context informs the argument . Does your material contain references to other sources, or imply knowledge of another subject matter? What meaning does the text attribute to such other sources? Exploring these questions will help you figure out what function  intertextuality serves in light of the overall argument.

8) Identify linguistic and rhetorical mechanisms

The next step in your analysis is likely going to be the most laborious, but also the most enlightening when it comes to exploring how a discourse works in detail. You will need to identify how the various statements function at the level of language . In order to do this, you may have to use additional copies of your text for each work-step, or you may need to create separate coding categories for your digital files. Here are some of the things you should be on the lookout for:

  • Word groups: does the text deploy words that have a common contextual background? For instance, the vocabulary may be drawn directly from military language, or business language, or highly colloquial youth language. Take a closer look at nouns, verbs, and adjectives in your text and see if you find any common features. Such regularities can shed light on the sort of logic that the text implies. For example, talking about a natural disaster in the language of war creates a very different reasoning than talking about the same event in religious terms.
  • Grammar features: check who or what the subjects and objects in the various statements are. Are there any regularities, for instance frequently used pronouns like “we” and “they”? If so, can you identify who the protagonists and antagonists are? A look at adjectives and adverbs might tell you more about judgements that the text passes on these groups. Also, take a closer look at the main and auxiliary verbs that the text uses, and check what tense they appear in. Particularly interesting are active versus passive phrases – does the text delete actors from its arguments by using passive phrases? A statement like “we are under economic pressure” is very different from “X puts us under economic pressure”… particularly if “X” is self-inflicted. Passive phrases and impersonal chains of nouns are a common way to obscure relationships behind the text and shirk responsibility. Make such strategies visible through your analysis.
  • Rhetorical and literary figures: see if you can identify and mark any of the following five elements in your text: allegories, metaphors, similes, idioms, and proverbs. Take a look at how they are deployed in the service of the overall argument. Inviting the reader to entertain certain associations, for instance in the form of an allegory, helps construct certain kinds of categories and relations, which in turn shape the argument. For instance, if I use a simile that equates the state with a parent, and the citizens with children, then I am not only significantly simplifying what is actually a very complex relationship, I am also conjuring up categories and relationships that legitimize certain kinds of politics, for instance strict government intervention in the social sphere. Once you have checked for the five elements listed above, follow up by examining additional rhetorical figures to see how these frame the meaning of specific statements. Things to look for include parallelisms, hyperboles, tri-colons, synecdoches, rhetorical questions, and anaphora, to name only the most common.
  • Direct and indirect speech: does the text include quotes? If so, are they paraphrased or are they cited as direct speech? In either case, you should track down the original phrases to see what their context was, and what function they now play in your source material.
  • Modalities: see if the text includes any statements on what “should” or “could” be. Such phrases may create a sense of urgency, serve as a call to action, or imply hypothetical scenarios.
  • Evidentialities: lastly, are there any phrases in the text that suggest factuality? Sample phrases might include “of course”, “obviously”, or “as everyone knows”. A related question then is what kinds of “facts” the text actually presents in support of its argument. Does the text report factuality, actively demonstrate it, or merely suggested it as self-evident? One of the strongest features of discourse is how it “naturalizes” certain statements as “common sense” or “fact”, even if the statements are actually controversial (and in discourse theory, all statements are controversial). Be on the look-out for such discursive moves.

9) Interpret the data

You now have all the elements of your analysis together, but the most important question still remains: what does it all mean? In your interpretation, you need to tie all of your results together in order to explain that the discourse is about, and how it works. This means combing your knowledge of structural features and individual statements, and then placing those findings into the broader context that you established at the beginning. Throughout this process, keep the following questions in mind: who created the material you are analysing? What is their position on the topic you examined? How do their arguments draw from and in turn contribute to commonly accepted knowledge of the topic at the time and in the place that this argument was made? And maybe most importantly: who might benefit from the discourse that your sources construct?

10) Present your findings

Once you have the answer to your original question, it is time to get your results across to your target audience. If you have conducted a good analysis, then you now have a huge amount of notes from which you can build your presentation, paper, or thesis. Make sure to stress the relevance , and to move through your analysis based on the issues that you want to present. Always ask yourself: what is interesting about my findings, and why should anyone care? A talk or a paper that simply lists one discourse feature after another is tedious to follow, so try to focus on making a compelling case . You can then add evidence from your work as needed, for instance by adding original and translated examples to illustrate your point. For some academic papers, particularly graduation theses, you may want to compile the full account of your data analysis in an appendix or some other separate file so that your assessors can check your work.

Mind the limitations:

Discourse analysis offers a powerful toolbox for analysing political communication, but it also has its pitfalls . Aside from being very work-intensive , the idea that you only need to follow a certain number of steps to get your results can be misleading. A methodology is always only as good as your question . If your question does not lend itself to this sort of analysis, or if many of the steps I list above do not apply to you, then come up with an approach that suits your project. Don’t be a methodologist : someone who jumps at a set of methods and applies them to everything in a blind fit of activism. Always remain critical of your own work.

This means being mindful of the shortcomings in your approach, so that you do not end up making claims that your material does not support. A common mistake is to claim that a discourse analysis shows what people think or believe (or worse: what entire societies think or believe). Discourse analysis is a form of content analysis. It is not a tool to analyse the impact of media on audience members. No amount of discourse analysis can provide adequate evidence on what goes on in people’s heads .

What we can learn from a discourse analysis is how specific actors construct an argument , and how this argument fits into wider social practices . More importantly, we can demonstrate with confidence what kind of statements actors try to establish as self-evident and true . We can show with precision what rhetorical methods they picked to communicate those truths in ways they thought would be effective , plausible , or even natural . And we can reveal how their statements and the frameworks of meaning they draw from proliferate through communication practices.

References:

Chilton, Paul (2004). Analyzing Political Discourse – Theory and Practice. London: Arnold.

Fairclough, Norman (1995). Critical Discourse Analysis: The Critical Study of Language. Harlow: Pearson Education Limited.

Jäger, Siegfried (2004). Kritische Diskursanalyse. Eine Einführung. (Discourse Analysis. An Introduction). 4th ed., Münster: UNRAST-Verlag.

Mayring, Philipp (2002). Einführung in die Qualitative Sozialforschung – Eine Anleitung zu qualitativem Denken (Introduction to Qualitative Social Science Research – Instruction Manual to Qualitative Thinking). 5th ed., Basel: Beltz Verlag.

McLuhan, Marshall (1964/2001). Understanding Media. New York: Routledge Classics.

Share This Post, Choose Your Platform!

About the author: florian schneider.

' src=

452 Comments

[…] and may want to take a look at my practical tips on how to set up a discourse analysis and at the ten work-steps I recommend for analyzing political […]

[…] contribute to and shape commonly-accepted truths in a society. Such a framework is useful for exploring truth claims and knowledge construction, particularly when the focus lies on who has the power to make certain statements, but it does not […]

[…] particularly in an age of mass-media: the visual. That is why, in this post, I expand the toolbox for discourse analysis that I have introduced in a previous post by adding methods and work-steps that will hopefully not […]

' src=

Thank you!! :)

' src=

No worries at all. Glad if it helped.

' src=

Hi Florian, I’m Danil, from Indonesia, currently working on research related to political discourse. I would really appreciate if you could provide me any information related to political discourse using Vandijk’s apporach’s Sociocognitive. Thanks for your kindness. All the best for u. Lookingforwards to hearing from u soon.

regards, DANIL

Hi Danil, If I understand you correctly, you are looking for authors who discuss Van Dijk’s work, right? I am not sure whether there is one single article that covers his work in general, but you might be interested in the various papers that have cited him or reviewed specific books of his. Here’s a short selection you might find helpful: http://connection.ebscohost.com/tag/VAN%2BDijk%252C%2BTeun . There might be others that you can get to via a targeted web search for articles with the tag “Teun Van Dijk”. Best Florian

' src=

Many thanks indeed Florian, So sorry, It has been very longtime since I’ve never looked over your reply. I’v searched for Van Dijk’s analysis model and got several books. One more thing, I’m and some friends have planned to organize a conference on Critical Discourse, we would really appreciate if you could be one of the key note speakers. We’ll inform you the exact time of the program. Cheers. All the best

Thanks Danil, glad I could be of help. Best Florian

HelloFlorian, I’v made contact with u several times and found your comments and suggestion were very helpful for me. I would really appreciate if you could help me with definitions and short examples of some terms used in Critical Discourse particularly which are introduced by van Dijk: Context models, mental models, experience models, event models. I got confused with their differences. Looking forward to hearing from u . Many thanks indeed Florian.

Best, DANIL

Hi Danil, I’m afraid I’m not familiar with van Dijk’s writings on these concepts. You’ll have to check his work and see what definitions he himself provides. Sorry to not be of more help. Let me know what you find out. Best Florian

Thanks any way Florian. You have been so helpful. I’ll try to find out the meanings of these stuff.

' src=

Hi Danil! Hope you must be fine. Well could you please share some of the work of Van Dijk that you have searched and also the research proposals and Research Articles/papers that you have in his domain. Because your area of research and my area of research are similar so we may help and support each other. Thanks in Advance

' src=

Hi Florian, I am a student of political science at stockholm university in Sweden. I have not had a clear answer from this question “Discuss what material could be included in the study when using discourse analysis (apart from the attached document). Motivate why the material you suggest should be included. I answered this question two years ago but need some clarification.

Hi Joshua, I’m not sure I can answer your question. This seems like a course assignment, so I assume your instructor had specific things in mind, based on what you’ve been doing in class. It’s hard for me to know what these were. Have you taken a look at my other blog post on setting up a discourse analysis? It includes the kind of questions one should ask while getting ready for such a project. I imagine some of these might connect with what you are expected to write: http://www.politicseastasia.com/studying/setting-up-a-discourse-analysis-of-political-texts-from-east-asia/ . Good luck with the assignment.

' src=

Hi Florian, Thank you for your summation of critical discourse analysis. I was struggling to get my head around this concept until I found your blog. Thanks to you I received a very high mark for my masters paper and I am referencing your work for my final masters dissertation.

I appreciate educators who use their vast knowledge to simplify important concepts. This truly is basics of all teaching.

Thanks again Cheers Mary

Hi Mary, Thanks for your kind words. This really means a lot. I’m very glad to hear that you found these materials useful for your graduate studies. Good luck with the write-up of your MA thesis! Florian

[…] my favourite:  ten work-steps on how to do a discourse […]

' src=

This is gold! I didn’t know how on Earth to start my discourse analysis assignment until I came across this. It has been a life saver. Wish my tutor had taken the time to break it down like you have. Thank you for sharing your knowledge.

Thanks Lauren, it’s good to know that this was useful in your studies. Good luck with the assignment!

' src=

Dear Lauren, I am international students in the UK and I also have assignment about discourse analysis excuse me can I have a look to your assignment, please

Best wishes, Nada

' src=

Wonderful, cogent, concise description of methodology. My students are thanking you!!

Thanks for the kind words, Patricia. I really appreciate it.

' src=

whoa!!! thank yuh i av learnt alot not jus the discourse analysis. ur such a life saver

That’s very kind of you Khadijah. Glad I could help.

' src=

This is the most clear and helpful post about discourse analysis I’ve ever read! Thanks a lot for sharing.

' src=

Yes, excellent. Many thanks. Reading all the Fairclough and Foucault in the world doesn’t resolve practical issues like how to cite the analyzed content. Does it belong in works cited? Footnotes? Appendix? I can’t seem to find a natural fit for my research.

This is a good question, Katie. The answer depends on how detailed your analysis of the materials is. I have seen undergraduate studies that cite longer sections in the main body and then list the source in the bibliography like any other materials. In some cases, particularly if the project analyses several texts, it may be good to have two sections in the list of references: one for “primary sources”, one for “secondary sources”. Personally, I like to see the materials that were analysed in an appendix (and then listed in the bibliography alongside the secondary sources). For graduate or post-graduate work, it might even be worthwhile expanding such an appendix to include practical work steps, visualisation of the data, or different rounds of coding on the same document. That way the reader (or examiner) can check the thought process behind the research. Just an idea. Hope this helps!

' src=

hi sir i am a master student from algeria , first thank you for the article it was realy helpful , second, i am working on female stereotypes in proverbs , since i have a collection of proverbs to analyse my teacher adviced me to ask you how to do so;would you please give me some pieces of advice analyse them .

' src=

How to do a discourse analysis by finding the cohesions and the coherences in the article?

Dear Putri, I am not sure I have understood your question correctly – do you mean: how can we study the structure of a text? If that is the case, I would try to identify what each paragraph or section does (e.g. does it functions as an introduction, an argument, a counter-argument, an example, a conclusion?) and would try to establish how the author transitions from one section to the next. You could also go into more detail and check what conjunctions or rhetorical tools the text deploys to provide a sense of flow (for instance, if I write: “Discourse influence language. So it also influences politics”, I have linked two separate claims in a way that is by no means self-evident). Some of the literature that I’ve provided in the list of references includes more sophisticated examples than I can provide here, but I hope these brief notes already help.

' src=

Many thanks from Latvia! I have struggled with discourse analysis for about month now and no one could actually tell me what’s it about and how exactly to do the analysis. This really was a lifesaver for my bachelors degree research. Thank You a lot!

Thanks Ilze, I’m glad you found this useful. Good luck with the BA!

' src=

Well done, Florian. This is very professional and very helpful. I am teaching discourse analysis to undergraduate business students and now I don’t have to create my own video.

Hi Tina, thanks for the encouraging words. Hope your students enjoy the introduction and video.

' src=

Thank you so much. This is so clear and useful for unpicking political text to illuminate power structures and motivations.

[…] widely as their subjects. For scholars interested in digital media content, methods might include discourse analysis, visual communication analysis, iconography, and various tools adopted from the study of […]

' src=

Hi, this article is so useful! I am currently conducting discourse analysis of a television travel documentary and was wondering how the stages can be adapted to fit this? Obviously I cannot transcribe and code the hour long programme, so do I therefore transcribe sections which I feel to be most significant and code these? Thanks!

Hi Heidi, This is a good question, and depends a bit on the length of the material and what you are trying to achieve. For cases where you are not interested in a shot-by-shot analysis, I would recommend creating a sequence protocol, and then coding those sequences. I recently did this with a colleague of mine to analyse a lengthy Chinese documentary, and it’s a good way to keep track of the content at a macro-level. You can then “zoom in” on specific sequences and examine them in more detail where it’s useful and necessary, for instance shot-by-shot, or transcribing what was said. I’ve written a bit more about this here: http://www.politicseastasia.com/studying/an-introduction-to-visual-communication-analysis/ The section on “working with moving images” in particular might be of interest to you. Best – FS

Hi Heidi! Hope you must be fine. Well could you please share some of the work that you have researched with reference to Television discourse and also the research proposals and Research Articles/papers that you have in his domain. Because your area of research and my area of research are similar so we may help and support each other. Thanks in Advance

[…] You may want to also take a look at my own discussions of methodology, for instance my blog post on how to do a discourse analysis (which is about methods) or how to set up such an analysis (which includes epistemological […]

' src=

Thanks a lot for the great work. I am doing a research on how Twitter was used in Zimbabwe during the 2013 elections. I have collected more than 80 000 tweets over 51 days. My question now is: With such a huge dataset, is it possible to do a proper CDA?

Hi Leonard,

This sounds like a fascinating data set, and I do think it is possible to do a discourse analysis on large amounts of text. However, I would only rely on quantitative tools to highlight keyword distribution and check the general thematic structure of the text corpus. I’d always then follow up by looking at representative (or outlying!) examples in more detail for the qualitative part of the analysis.

By the way, it might also be interesting to see how the people who post these tweets are connected on Twitter, and what kind of networks consequently provide the foundation for the discourse you’re looking at. I can recommend the work by Richard Rogers over at the University of Amsterdam on how to get a handle on such digital methods questions. Oh, and then there’s the very tricky question of reproducing your results without singling out the various posters – if you haven’t read it yet, I can recommend Zimmer’s article on this issue ( http://bit.ly/1hqf1T5 ). Just FYI. :)

Good luck with this fascinating project! Do let me know what you find.

Best – F

' src=

Hi. i found this article very helpful. Thanks a lot! I’ve got an assignment on media political discourse. Please, i want to know if making use of critical discourse analysis will be an excellent way of analysing a newspaper article.

Hi Nassy, This all depends on the kind of questions you have regarding the newspaper article. If you are trying to find out what it’s position on a specific issue is, and how the author uses language to establish that position, then a discourse analysis might be worth a try. If you are only looking at a single article, though, I’d be careful not to overstate how that piece contributes to broader discourses. That would require either a wider study, or more information on how relevant this particular article is. As with any other subject area, the success of a paper very much hinges on the research question. The selection of methods (for instance: discourse analysis) should follow from that. All the best Florian

' src=

Hi, thanks a lot for this article :) I am actually doing my dissertation on the role of media in environmental protection- using renewable energies…i’m using discourse analysis to analyse newspaper..but i’m a little bit confused..should I separate the analysis of my themes from the grammar part or I mixed both? :s Thnks in advance

Hi Mau, thanks for the question. I think it depends on the kind of dissertation you are writing, and the level of linguistic detail you plan to go into. If you are working on a research MA or PhD, and have a lot of data, then it might indeed be a good idea to write a chapter that collects and discusses recurring grammatical features in the texts, and to then follow this up with a chapter that discusses what discursive positions are constructed through the language (with examples, of course). To be honest, I myself like it when a thesis tells a story, so I would be tempted to combine these two things: you could structure your thesis according to the different themes you are analysing, and then use the grammar parts as evidence and illustration. In a case like that, you could also provide the more technical details and any primary sources in an appendix, so you can readily reference your analytic work without having to reproduce every minute bit in the main text. So as you see, it’s a matter of preference. I would check with your supervisor to see what makes most sense for your case, and whether your examiners have a preference in this regard. You are, after all, writing for a specific audience… Hope this helps! Best- F

' src=

Hi Florian,

Your material is very clear and helpful. Are these methods okay to use for interviews that have been written up at masters level. Many Thanks Josh.

Hi Josh, Discourse analysis is definitely a great way to process interviews – provided you are looking for the (often subconscious) communication choices your interviewees make to get their point across, and if you want to know what kind background knowledge and assumptions informs their views. It’s often quite revealing to see how interviewees tie their arguments together with wider social discourses and the argumentation patterns you’d find there (e.g. the news, academia, work conversations, etc.). What I normally do is create a protocol of the interviews (using either my own paraphrasing or rough transcripts), and after coding the meaningful segments I look at specific parts in detail. This can then also include transcribing those parts in a way that marks hesitations, intonations, and other such qualities of the spoken word (Paul Chilton has provided some useful annotation advice for this). As you might imagine, this can be a lot of work. So if you are mainly on a “fact finding” mission and are trying to figure out how the topic “works” that your interviewees discuss, then I probably wouldn’t recommend a full discourse analysis based on transcripts: simple protocols might be the better way forward. I hope this helps you decide how to approach those materials – good luck with the MA! Best – Florian

Thanks for the replay and advice, this sounds really good. I think I’m going to use some other material as well such as a short film. So mix the discourse analysis with the visual analysis that you have also clearly presented. Could I call this a multimodal discourse analysis? I think the wider context filters through in the interviews and the short films quite well, I also think this is physically impacting on society and possibly playing into Foucault’s ideas about Govenrmentality. Would you recommend analysing some of the physical impacts as well?

In relation to Paul Chilton is there a link to an example of how to transcribe in a way that marks hesitations, intonations, and other such qualities of the spoken word?

Many thanks in advance Josh.

I am always in favor of including other types of media, and seeing how a discourse works in different “modes”, so this sounds promising. I would be careful to call something a “multimodal” analysis, though: I think the word fits best when you systematically look at how the medium contributes to the discourse. So if you are analysing camera angles, mise-en-scene, editing, etc. in combination with what is said in the film, then the term applies. If you are mainly commenting on the content of the film in relation to your interviews, then I might try to find another word (or point out in a footnote that you are not conducting a full-fledged “multimodal” analysis, and then suggest further reading on that kind of research approach).

As for “physical” impacts, I find it fascinating to see how discourses crystallize into institutions and then inform such things as buildings, urban planning, use of physical violence, etc. Is that what you have in mind with “physical” impact? That would be the sort of question Foucault indeed looked at. My advice here would be to include such issues if you have good data, and to otherwise note such impacts in the intro/conclusion of your thesis. A risk here is that you might end up doing too many things at once, so be careful that you still narrow down your main analysis enough. This, of course, depends entirely on the kind of thesis you are writing.

As for the transcription advice, I couldn’t find Chilton’s notations online, but I have reproduced some of them in the figure in this blogpost: http://www.politicseastasia.com/studying/discourse-analysis-and-foreign-languages/ . As you’ll see, the full list of notations is on page 206 of his book “Analyzing Political Discourse”.

Let me know how your analysis proceeds!

Best – Florian

Hi Danil! Hope you must be fine. Well could you please share some of the work that you have researched with reference to television discourse and also the research proposals and Research Articles/papers that you have in his domain. Because your area of research and my area of research are similar so we may help and support each other. Thanks in Advance

' src=

Hi Florian, Firstly, this post on discourse analysis is incredibly helpful so cheers for that! I’m embarking on my MA dissertation and am doing a critical discourse analysis. I’ve focused on one online newspaper and its coverage of immigration, but am feeling overwhelmed by the amount of data generated. I was wondering if you could recommend how many articles to study, as CDA is so intense, would 2 or 3 be ok? Thanks Michele

Hi Michele, I agree that a full discourse analysis of a large number of texts is almost impossible for anything smaller than a research MA or PhD thesis. There are three ways you can still make a contribution in an MA thesis, but without overwhelming yourself. The first is to consciously phase out certain analytic aspects, for instance by choosing to not explore all linguistic features of the texts in detail. In that case, you’ll have to find a good justification for your choices, and should probably point out at the end what follow-up research would now be necessary as a next step. The second option is to chose materials that are particularly representative. If you have evidence that a particular newspaper article kicked off a huge debate, or that a specific policy document is of paramount importance (e.g. a state-of-the-union address, etc.), then you may not need more materials – you should, however, then point out what limitations this particular “window” into the discourse has. Thirdly, you could take a classic hermeneutical approach by starting with one text, qualitatively mapping out the discourse and its features there, and then moving on to a second text, a third text, and so on, until you are no longer finding any major new discursive features. I believe Jäger recommends such an approach. If you narrow your topic down well enough at the outset, you may indeed be in a position to justify using only a handful of texts rather than a large corpus.

Thanks Florian, this is really helpful. I’ve decided to do a general analysis of headlines from one month of the newspaper I’m using, focusing on elements of structural feminism and critical discourse analysis, and then shall do a more detailed breakdown of 3 of the most relevant articles. Do you think that would be okay, as long as my limitations are explicit? Thanks Michele

This sounds very cool – I personally like approaches that take a bird’s-eye view first (through headlines in newspapers, structure of TV series seasons, etc.) and then pick a representative sample for detailed analysis, based on that initial work. This could work nicely. Let me know how it shapes up! Best – F

' src=

It’s a good article to teach people how to conduct one discourse analysis. I learn much from it when I designed my research. I just have one question, is it suit for analysing the official documents like rules, laws, regulations etc.? I think it is good for assessing news, but I’m not sure if it can be applied to some official papers.

Discourse analysis can definitely be used on policy documents. It is indeed easier to analyse news articles, since they are often rather explicit about their “discursive position”, but legal texts also appeal to certain categories, draw from assumptions, and establish self-evident truths. The important thing to keep in mind is that a legal document is a specific genre, and that different genre conventions consequently apply. I know Fairclough has looked at official documents, and you’re likely to also find such studies in the established journals as well (e.g. Discourse & Society), so I’d recommend taking a look at such examples for inspiration.

Hi, Florian, Thank you for your suggestion. I have read Media Discourse written by Fairclough. His ‘three-dimensional method of discourse analysis’ is more suitable for my dissertation. I find it’s difficult to apply the theory without any comparison. In addition, due to the translation, I find it’s hard to conduct it to Chinese documents. Could you mind to tell me how you deal with this situation? Thanks a lot!

Translation into different contexts is of course an issue, particularly with the more theoretical aspects of discourse analysis (Fairclough is a good example here). You could check what Chinese authors are writing on the subject. Shi Xu from Hangzhou’s Zhejiang Daxue is a pretty big name in that regard, and he’s been criticizing discourse analysis for being a “Western” method that needs to be revised for use in China. I don’t particularly agree (see my discussion here: http://www.politicseastasia.com/research/fourth-international-conference-on-multicultural-discourse/ ), but as you can see a straight-forward application of Fairclough to a foreign context deserves critical reflection. Maybe Shi’s work, or that of his students, can provide the comparison you are looking for? In addition, I’ve written a bit about how to do a discourse analysis in practice when using foreign scripts, but I’m not sure this answers your question: http://www.politicseastasia.com/studying/discourse-analysis-and-foreign-languages/ .

Sorry for replying late. Thank you so much for providing so many useful resources for my research. I selected English texts as my resources to apply CDA (because I don’t need to translate it).

Many thanks for your suggestions.

' src=

Hi Florian, this is a great article, and is one of the first I read that really explains the process clearly. I’m looking for some advice, however, as I’m writing about improving the nuclear Non-Proliferation Treaty and I’ve decided to conduct a discourse analysis on the Treaty, the IAEA statute and political speechs made by America as well as non-Western orgs such as the NAM or the Arab League on the subject, to compare discourse between the two sides and how to bridge the gap. Is this too ambitious? How many documents do you recommend for a decent analysis, and of such variety in genre? Thanks

Dear Louise, This does indeed sound quite ambitious – I assume you are writing an MA thesis? You may want to take a look at my comment above, on Michelle’s project. She had similar concerns about narrowing down her material, and I suggested three different options to her on how to handle that challenge. In your case, you may have to make a choice: you could use one of the two sets of texts (official IAEA documents vs. speeches) as background and the other to do a detailed analysis. For instance, I think doing a discourse analysis on the actual Treaty and the IAEA statutes makes good sense, and should be doable at the MA level. On the other hand, if you want to cover speeches, I would probably recommend taking a quantitative approach first, for instance using WordSmith ( http://www.lexically.net/wordsmith/ ) or some similar tool. You could then “zoom in” on specific features of the discourse and discuss those in more detail. Otherwise you might end up with a lot of speeches, particularly on a topic such as this one, that you may not be able to assess in detail at a qualitative level. Unless of course you are doing a PhD, in which case this sounds like the kind of work that would make a good doctoral thesis. I hope this helps! Best Florian

Thanks Florian, I’m looking into WordSmith now. After reading your response for Michele as suggested, would you recommend Fairclough over, say, Foucauldian Discourse Analysis, for such a project? What would you say are the benefits of CDA?

Hi Louise. I’m afraid it’s not all that easy to draw a clear line between different approaches to discourse, such as Foucauldian analysis, CDA (e.g. Fairclough), political discourse analysis (e.g. Chilton), or discourse-historical analysis (e.g. Wodak). They often overlap and draw from each other, and many of the distinctions are subtle theoretical differences (for instance how “constructivist” the respective author is) rather than completely different methodological approaches. For a good introduction of how Fairclough aligns himself with Foucault’s aims, I can recommend this short text: http://bit.ly/1t3Nii7 . To answer your question, I think you could make a distinction at the methodological level between studying 1) primarily and in great detail the linguistic features of a discourse, 2) the socio-historical context of the discourse (and its development over time), and 3) the strategic communication choices and social practices of different actors at a particular point in time (e.g. framing, self-other representations, etc). Most discourse analysts will look at all three, and if you want to read a good article that covers all of these angles for the Scottish case, I can recommend this piece by my colleague Johnny Unger for inspiration: http://bit.ly/1ksxj9v . For an MA thesis I think it would be fair to emphasise one of these levels of analysis, as long as you also acknowledge the others. You could, for instance, provide the socio-historical context in your introduction and could then explore how different actors frame the issue, building in examples from the language as you go along. Your limitations/future research section in the conclusion could then point out how more detailed linguistic analysis and historical tracing of the discourse can shed light on additional questions you have raised in your thesis. Just a thought.

' src=

Hi Florian. Thank you very much for putting this website up. I am currently writing a proposal for a PhD dissertation on energy policy formulation and have been wondering about a specific kind of discourse analysis — argumentative discourse analysis (M. Hajer). There seems to be a dearth of resources about it, especially as a method. I surmise that it emphasizes certain dimensions of discourse compared to the “conventional” discourse analysis which generally explores/examines text (linguistic), the rhetorical component, as well as context (socio/historical). I was wondering if you were kind enough to offer suggestions or tips (perhaps even some general “red flags”). Many thanks and more power to you.

Hi Jalton, Sorry for keeping you waiting – you caught me during the Easter break. Thanks for pointing out Hajer’s work, which I think has a lot in common with the issues I’ve discussed here. At the risk of doing his work injustice, it seems his argumentative discourse analysis (ADA) is very much interested in the structure of texts and conversations and in the rhetorical and argumentative strategies that people deploy. For instance, he’d be interested in classic argumentative fallacies such as appeals to authority or begging the question, which I agree are very useful when examining arguments. I particularly like the fact that he places a strong emphasis on how people perform their role in social interactions, which is something my colleagues and I are also interested in. In that sense, I don’t think ADA stands in opposition to other forms of discourse analysis – it simply draws attention to specific aspects of communication and would probably fit very nicely into the “toolbox” I’ve put together above. There are of course also differences, for instance in the way Hajer writes about “discourse coalitions” when talking about groups that share similar discursive positions – a context in which I would probably use a network approach – but these distinctions are rather subtle. I would have to talk to him and his colleagues to see where we potentially disagree. My guess would be that I place a tad more emphasis on agency whereas he might be a bit more interested in structures. At any rate, something I find highly valuable is his definition of “dominant” discourse, which you’ll find here: http://www.maartenhajer.nl/?page_id=14 (under “influence of discourse”). Not sure whether I’ve helped or muddied the waters further… let me know what your PhD research uncovers, and what sort of approach you ended up adopting. The project sounds fascinating. Best – F

Thank you for taking the time to respond. It is very much appreciated. I have taken the time to go through all the “nooks and crannies” of this website and what a rich source of ideas and methods it truly is! I hope that one day, I get to attend one of the conferences and workshops which your organization is organizing (since my other research interest also touches generally on the socio-political dynamics of the digital media, representation vis-a-vis Filipinos/Philippine culture and nationalism). Keep in touch :)

Thanks Jalton, I appreciate the feedback. Hope you’ll get to join us at one of our future events – sounds like your work would fit right in. Let’s do indeed stay in touch!

' src=

Dear Florian,

Thank you for the helpful breakdown of such a complex task! I’m also working on a PhD, dealing specifically with larger discourse concepts of nationalism, economic development and globalization in east Asian developmental states (SK, Taiwan, etc.).

I rely heavily on Jessop’s Cultural Political Economy (CPE) approach as well as Fairclough’s CDA, and I would like to investigate the shifting of discourse with concepts such as re-contextualization (such as competitiveness of economies to the concept of national identity, etc.) The problem I have is coding the samples. I have narrowed down my codes but the relation between larger concepts such as discourse of globalization/nationalism and smaller ones [branding as advancing in international division of labor] seem somewhat arbitrary. I know this totally depends on the research question, but how I can I work coherently without becoming muddled with the infinitely interconnection relations between these concepts?

I appreciate your reading this!

Sorry for the late reply, but only just got back from a trip. Getting the amount of work you put in right is indeed a big challenge. I think there’s three things you could do, but I’m not sure how much each option applies to your case. Nevertheless, maybe you’ll find some of these ideas useful: The first is to use “evolutionary” coding to come up with a long and comprehensive list of categories, which you then apply to your materials, but that you don’t necessarily all examine in the thesis. The work might be more arduous now, but if you plan to use the materials after the PhD as well, for follow-up work, then this might be a good option for you. It sounds to me like this is the direction you are already headed in. In the thesis, you can then look at specific discourse strands only, but note that they of course intersect with other issues as well (and point to the appendix for the comprehensive list). Making choices as to what is most important is part of a PhD project, so I doubt anyone would fault you for not covering every conceivable discursive connection. The second option would be to come up with a two-step coding process: the first part would work at the macro-level, and would use units of your materials that are fairly large (so: full texts, full pages, or at the very least full paragraphs). You can create a table and then list all the relevant units, followed by all the various codes you have decided to use for that section, and maybe also deploy quantitative tools to then help you get a grasp of that material. The second step would then be to select segments from that first “bird’s-eye-view” step that are particularly important to your project, and to go in and do the detailed coding and qualitative analysis there. The third option would be to state at the start that you are only interested in two or three main concepts, and to radically narrow down your set of categories. Whether or not this is feasible (and to what extent it is advisable) is something I can’t comment on, but a decision you would have to make together with your supervisor, based on your materials.

Sorry for not having better advice – this is a very difficult question. Let me know how you decided, and how the project worked out!

' src=

This brilliant! While many say there is no set of methods in DA, this gives us a great starting point to assess and use on our specific studies. Thanks a million, you have summed up hours of reading!!

' src=

Dear Florian, I found these information very very important.I’m doing a phd on disasrer communication. I’m looking at how communication channels,specially social media have been used to build community resilience to natural disasters. I wish to do a discourse analysis on interview data, with disaster managers and communication managers. This is a comparative study about Sri Lanka and New Zealand. I think this data analysis method fits with my objective, I need to see how the meaning of being resilient is build through the communication channels in these two countries. I appreciate your thought.

Dear Gayadini, sounds like a great project. I particularly like that you’ll be checking up on “resilience” discourses. I would keep my eyes open for concepts that your interviewees link to that idea, and for the argumentative strategies they use to make sense of disasters and (personal) responsibilities. To me, the whole “resilience” story is decidedly neo-liberal, since it transfers the burden of being prepared for risks and reacting to crises to local communities, households, or individuals. Would be fascinating to see whether this impression holds in the two cases, and what the nuanced variations might be.

Coincidentally, my colleague and I have just published an article on PRC disaster discourses in the Journal of Contemporary China (2014, vol.23/88). The study is not a linguistic discourse analysis, and does not examine resilience, but it looks at visual discourses in official and popular culture, which might nevertheless be interesting for you: http://bit.ly/1mO2gsi . All the best – F

' src=

Dear Florian, Thank you so much for your helpful article, if before I had only confusion in my head now is everything clear!Now I have a start point. I am dealing with my MA thesis on discourse analysis, more specifically discrimination in discourse about Romanian people in an Italian newspaper I’ve chosen. Till now I have 9 articles, I guess is too much; last Thursday I had a presentation with my two supervisors and they told me I’ve done too much linguistic analysis, and I shall focus more on microparts that I consider extremely important and proceed with the discouse analysis supported of course by the linguistical analysis. The problem is that there is no direct discrimination against Romanians expressed in the articles I’ve chosen and they told me I shall focus on the suggestions and inferences that come from the report, the ones I understand the journalist is reporting, or is in some way influenced by others/society/rules of the newspaper, somehow what shall I do is to read between the lines. Do you think I can apply your whole explanation from above to my case? I have difficulties dealing with this. They told me that even if I don’t prove at the end the Romanians are discriminated in that newspaper (it may be possible to prove or it may be not) it is sufficient for the requirements of the MA to know how to handle with discourse analysis. Do you have any suggestion to tell me how to deal with this? Do you think 3 or 4 articles would be enough for 100-120 pages?

Thank you and greetings from Denmark!

Dear Daniela,

I am not particularly familiar with the MA regulations in Denmark, but I would say that following a supervisor’s advice is always a good call. For an MA thesis, I can completely understand that they want you to contextualize your sources in wider social practices, and that providing a few key examples at the linguistic level is sufficient. Personally, I am satisfied when students demonstrate that they can pose a clever question, select materials that promise to address that question, and then try to use an academic method on those materials. If the results don’t cover the whole issue in all its complexity, that is usually quite alright, particularly if the thesis recognizes these shortcomings and can give suggestions for further study. You are, after all, not writing a PhD thesis…

Take a look at my discussion above with Louise and with Mihn – they had similar concerns about the scope that a discourse analysis at that level can realistically cover.

What I would probably do in your case is take all nine articles and go through them rather coarsely, noting the main themes that characterize that particular debate. I would then go back to particularly representative or simply very noteworthy examples to show how these features manifest themselves in the language and the argumentative strategies, but I would state clearly that your goal is not to conduct a full linguistic discourse analysis (…something that future research could explore in more detail). I would then focus on the image of Romanians that gets constructed in the articles, and the social/production context within which the articles make their case. If you end up finding that there is no stereotyping in your materials, that is in itself also a finding.

Hope this helps! Good luck with the thesis. Regards – F

Dear Florian! Sorry to disturb you again but its really important to share it with you. Please also guide me I want to present my Research paper on discourse analysis. S if you could find some platform like national or international conferences i would love to present there.

Thanks you very much in advance

Dear Florian, Thank you so much for your detailed answer! I will definitely follow your suggestion! I really appreciate the help you offer within this blog! Best regards, Daniela

' src=

I have to say your article is very enlightening. I am required to do genre and register analysis (Tenor, Field, Mode) as part of my MA (Linguistics and Translation). We call this source text (ST) analysis and we are required to carry it out before translating the ST. To be honest with you, I am only doing this analysis as it is an essential part of the end of the year project as translation theories and register analysis are completely useless when it comes to the actual act of translating. This is why I fail to see the point behind engaging in such an activity. However, after reading your article and watching the introduction, I am beginning to understand the idea behind DA. How does register analysis fit in DA? Is it possible to analyse register without doing the whole shebang (DA)?

You said in your article that ‘Passive phrases and impersonal chains of nouns are a common way to obscure relationships behind the text and shirk responsibility’. How is one supposed to know these analytical clichés? My analysis might lead me to find many passive phrases but I would never be able to make the connection you made. Why is it so difficult to find actual lengthy examples of discourse analysis?

Many thanks in advance Florian. Alex

Dear Alex, I think it makes good sense to consider register when analysing discourse, particularly where speakers (or writers) shift the level of formality they use in order to cater to different audiences. But it would very much depend on the case and the research question. If you are looking only for this particular element in a discourse, I could completely understand if you excluded many (or even all) of the steps I’ve outlined above. You would basically be looking specifically for contractions, elliptical phrases, etc. to draw your conclusions. As always with discourse analysis, I would only use the tools that help you do that, and would exclude the others. As for the conclusions that are worth drawing from language use, this is very much a matter of context. For instance, not every passive phrase obscures who the actors are in a sentence, but it isn’t far fetched to conclude that a text that painstakingly omits any reference to agents creates a certain impression of how the issue at hand works. I would always check what a particular linguistic choice achieves in a particular setting. As for good examples of discourse analysis, my personal favorite is the German book I reference above (by Siegfried Jäger), but there are plenty of good examples in English as well. Fairclough’s collection of essays is a classic, and it does include a few practical chapters. You could also check the journals Discourse & Society and Discourse & Communication – as with all academic journals, you’ll get a mixed batch of articles, but some of those analyses might serve as inspiration. The editor Teun van Dijk also has a website that includes additional resources: http://www.discourses.org/ . Hope this helps! All the best Florian

' src=

Its an amazing article in breaking down the complex process of DA into tangible doable steps. I came across it while trying to figure out how to do a CDA of news interviews televised during prime time on news channels, im recording from public and private channels in the Pakistani context. I had set out thinking initially when I developed my PhD proposal, that I would do an analysis of how the presenter/ anchor of the political talk show (I term it a talk show due to the infortainment aspect of these televised political interviews) frames the topic in the initial opening and check the closings to see if he maintained his original idea about the topic or the course of the debate or discussion. later one of the experts from the field suggested I need to see what patterns of control are exhibited in the intervening part as well.

Now that I’m recording the actual shows I’m confused and want to fine tune my focus, but there is just too much going on that i want to look into and at the proposal stage i made such wide ranging questions that I’m at sea with my analysis. where to begin? how to begin? Your suggestions seem so interesting. I was wondering what kind of suggestions you would give somebody who had thought at the proposal level that they had everything down and figures and now find that all aspects need to be re-thought.

Thanks for your article once again and thanks for any suggestions you might give to me.

Warm Regards Saira

Dear Saira, I think it is quite normal that a project changes between the early proposal stage and the actual analysis. In fact, that is a good sign: it shows that your analysis of the materials is defying many of the assumptions you and others previously had, and that this now necessitates difficult re-thinking of the topic. Personally, I would always try to start by structuring my materials at a “macro” level, for instance by looking at the different elements that a talk show uses. I would then try to figure out what features are particularly prominent in each element, and I would then build my methodology based on that. So, for instance, if recurring elements of the show are videos that introduce the guests, then I would think about doing shot-by-shot analyses of various such videos. If there are talk rounds in which a host moderates a discussion, I would take a look at how the host frames that discussion, and how he or she intervenes to guide the discourse in certain directions. These are just examples, of course, but maybe they already help a little bit. Again, I don’t think that re-working your research approach in light of the materials is a weakness – if you are open about that process (and, ideally, write a research protocol to keep track of how your choices evolved throughout the project), then it can very much be a strength. It shows that you are doing your job. All the best Florian

Hi Saira! Hope you must be fine. Well could you please share some of the work that you have researched with reference to television discourse and also the research proposals and Research Articles/papers that you have in his domain. Because your area of research and my area of research are similar so we may help and support each other. Thanks in Advance

' src=

I needed to thank you for this good read!! I definitely loved every bit of it. I have you saved as a favorite to look at new things you post…

' src=

loads of thanks :)

' src=

I have been reading the various links on Discourse Analysis that you have put together in your website, and they are awesomely helpful! I am in research of help and guidance because I intend to pursue a PhD on Linguistics, and I plan to focus on sociolinguistics, pariculary DA.

Right now in my country, the Philippines, there is much excitement, drama, action going on in our politics, with some of our Senators, who previously were showbiz actors, are being jailed and surrendering themselves due to plunder, and all sorts of corruption. I don’t know but I am appalled by all these political happenings in my country for the last few months and the recent years. ( I have been away since May 2012.)

I plan to use the online posts articles of the ABS-CBN, a major TV network, and the online version of the Philippine Daily Inquirer, a top national broadsheet, as a source for my discursive statements, and the “surrender” of Senator Bong Revilla last week as the discursive event. All these lead to the Napoles scam, which I think is an octopus of controversy besetting my beloved country.

In relation to work, another idea I have in mind is DA as applied to Tourism… Macau, as they say, is the Las Vegas of Asia, and there many interesting things going on here too in terms of tourism. That is the area I might be really see relevance, because in terms of Macau politics, I am not well-versed as I have just settled here during the Chinese New Year. I have an interest on this topic because I teach in a tertiary school offering solely tourism courses.

I would need your opinion about this and your advice on how to go about my Preliminary Proposal, as this is the requirement for admission to a graduate school I have chosen in Hong Kong (I teach here in Macau).

Do you think one of these will be interesting topic for a PhD study?

I hope this is not too much to ask, but your thoughts on my query are highly appreciated.

Thank you and keep up the good job you are doing! These are immensely valuable!

Best regards, Chloe

Dear Chloe,

Both of these topics sound doable, and I’m sure each would make for a good PhD thesis. I could imagine that the Philippine politics topic would be more timely, and you clearly already have thought about the methodology and your sources. It looks to me like this is a project you can easily write up in about 2000 words. The main challenge will be to stay as unbiased as possible. One of the reasons I don’t research German politics, for instance, is that I am not confident I would be able to keep my personal views out of my analysis. On the other hand, who would be more qualified to take apart the recent developments in the Philippines than someone who knows the country intimately but is now studying it from a distance? This could work very well. (…one other thought: have you considered looking into social media discourses on the subject? would be interesting to see how the discourse plays out beyond the official broadsheets and TV channels). As for the Macau topic, if you decided to go this way, you could interview officials from the tourism board as well as professionals in the industry to see how they market Macau as a brand. Those interviews, together with promotional materials (videos, web content, etc.), would make for a great set of sources that you could conduct a discourse analysis on. Hope this makes sense. Good luck with the project!

' src=

This is very nice. But is there any way I can ask for an example of this steps? :) Thank you.

You’re right, it would be nice to provide more examples. Sadly, I don’t have anything concise available at the moment. I’ll keep my eyes open. For now, my advice would be to look at some of the leading journals in the field and see what inspiration you might get from their articles. Discourse & Society and Discourse & Communication are two of the most famous outlets.

' src=

This is a great article. I don’t study linguistics myself but this was still helpful in getting an understanding of DA.

My wife is currently struggling with coming up with a topic for her term paper using Critical Discourse Analysis, she wanted to do an analysis on the strife in Palestine but doesn’t know where to start. I will have her read over this article and hopefully it will be helpful for her. My main issue is that I would like to be able to offer her some assistance so I’m doing research on how CDA works.

If at all possible, could you explain how one should go about analyzing online news articles which cover the war; and possibly where the best sources could be for this material.

Also any examples of work done by you or others on similar topics would be greatly appreciated.

I look forward to your response as soon as possible as her paper is due on the 29th of july, its only 12 pages so a couple days of works is all that’s necessary for the write up but the information gathering is where the real problem lies.

Thanks again

Thanks for your questions! I just saw your wife’s deadline is tomorrow – sorry for the late reply, but I’m abroad on research at the moment and don’t always see the notifications on time. I don’t have any good advice on where to find news articles on Palestine, since I myself am not working on issues in the Middle East, but I would always recommend also looking at the medium itself alongside the actual (often written) discourse. There’s an interesting paper on how to analyse websites that I would normally have recommended (I was thinking of John Knox’s 2009 paper “Punctuating the Home Page: Image as Language in an Online Newspaper”, which appeared in Discourse & Communication 3/2, 145-172), but it’s probably a bit late for that. I hope the paper goes well!

I am struggling with my Master thesis on the discrimination of Romanians in an Italian newspaper. I’ve found a very interesting article, but is mainly an interview, and the interesting discourses to analyze are expressed not by the journalist who asks the questions, but by the head of the police, in direct reported speech. How can I carry out the analysis on an interview if the journalist is not so present? thank a lot!

' src=

hello Florian, your article was so enriching. I am presently working on my MA dissertation and the topic is A discourse analysis of language use on social media. I actually want to concentrate on facebook, could you tell me on how exactly to go about it. Thanks as i anticipate your favourable response

Hi Esther, Studying Facebook is a difficult subject, since the functioning of social media brings with it all sorts of analytic and ethical questions. For instance, you’ll have to justify which FB pages you’ll be analysing and why. If you use the posts of people you have “friended”, then this raises ethical questions about their consent. If you use FB feeds from official institutions or enterprises, you can side-step that problem, but you’ll still have to justify your choices, of course. Then there’s the question whether you are focusing first and foremost on language use or whether you are willing to take into account the specifics of the medium. For instance, does your analysis look at “likes” and “shares”? Does it take into account what appears on someone’s wall and why? These may seem like trivial issues, but things gets complicated (and often quite technical) very quickly when you ask how the technical features of FB or the various social linkages of users or FB’s largely invisible algorithms end up shaping discussions. I don’t have good answers for how to deal with these issues, but you might want to take a closer look at the research that scholars are currently doing on FB and other social media. Good sources for this are the academic journals New Media & Society as well as Information, Communication & Society. I hope this helps! Good luck with the project.

' src=

Thanks for such a practical and helpful guide. But sir, are these linguistic and rhetoric mechanisms all that one needs in doing a Critical Discourse Analysis of texts also (say a religious or political text)? If no, please what are the linguistic and IDEOLOGICAL devices one needs to do a CDA analysis of political interviews in particular, especially using Fairclough’s approach?

Dear Amuuts, thanks for the kind words. As for your question, I wonder whether I understand you correctly: you are asking how to move beyond the linguistic and rhetorical features of texts and explore how they tie in with broader worldviews, right? The reason I ask is because the term “ideology” gets interpreted in vastly different ways. Fairclough is fairly Marxist about his use of the term (so he sees ideology as false knowledge), but other scholars at times use ideology either as a synonym with discourse or to signify a systematic framework of thought, carried by discourse (I would subscribe to that last definition). Either way, exploring the ideologies that communication practices relay is a core part of discourse analysis. So to explore the ideologies that get promoted through a text like a speech, you could isolate all statements on a specific subject, check whether they are part of a system of interlocking assumptions or beliefs, and then see whose interests these assumptions serve. It would also make sense to compare such statements to those in other sources, to see whether a speech perpetuates a particular ideological view (e.g. neo-liberalism or socialism). More generally, I would first recommend taking a look at the scholarship on ideology and to define what you mean by the term (and how you think ideology connects with discourse). Good sources for this are Terry Eagleton’s book ‘Ideology: An Introduction’ and Raymond Geuss’ ‘The Idea of a Critical Theory’, just FYI.

Thanks for your prompt response sir. But to be more specific, my research has to do with “Ideological Projection in media interviews with selected political party leaders” and I intend using Fairclough’s cda approach as my theoretical framework. My confusion now is that I’m not clear with Fairclough’s analytical tools (the ideological devices in the interviews) like the way van Dijk has listed his in several materials I have consulted. could you please help to itemise Fairclough’s analytical tools or would you advice I change my intended framework?

Dear Amuuts, I’m afraid I can’t help you itemize Fairclough’s analytical tools. You would have to get in touch with him. The reason I drew up the steps for this article was that I felt many CDA frameworks were not very explicit on what practical work steps they would recommend to study a text. This, to some extent, also goes for Fairclough, if you ask me. If going over Fairclough’s work does not answer your questions, then it might indeed be better drawing from someone else’s writings, or coming up with your own tools.

Hi Ammuts! Hope you must be fine. Well could you please share some of the work that you have researched with reference to television discourse and also the research proposals and Research Articles/papers that you have in this domain. Because your area of research and my area of research are similar so we may help and support each other. Thanks in Advance

' src=

It is really concise and useful. Thanks a lot!

' src=

dear,i find ur work fairly impressive and helpful.my research topic is ‘influence of cartoons on children’a critical discourse analysis from fairclough’s perspective.the main areas of investigation will be power relations,culture,violence,sexuality and other themes other than gender roles..kindly help me with useful tips

' src=

Hi, what’s the difference between this and a Critical Discourse Analysis?

Thanks, it’s been really useful, and thankyou for the advice for using Tagxedo,

' src=

Thank you for taking the time to do this and for sharing it publicly. As a distance MA student with no prior knowledge about CDA or guidance on my degree program, this is like discovering gold. It gives me a sense of direction that Fairclough’s texts do not offer, but can certainly be adapted to work around.

' src=

Great effort, really informative. I have been reading about DA for monthes. Books, papers, attending courses…etc. I understand the concept and the theoretical debates, but couldn’t find any sufficient guid to explictly declare a step by step approach! Thank you very much.

' src=

Thanks Florian. You are an angel. God bless!

[…] can move on to analyse your data in earnest. If you need tips on how to do this, take a look at the ten work steps I […]

' src=

Hi Florian, I am linguist used to analyze Classical Chinese Texts in terms of syntax and phonology, but I am now co-teaching a course on Critical Discourse Analysis at the Polytechnic University of Hong Kong and I would like to lead my students in conducting analyses of the media discourse about Occupy Central and related issues (opinions about police violence, disruption of public order etc). Op-eds in the South China Morning Posts are an easy start, but I would be interested to cover cantonese newspapers. My students told me that, for example, now most of the pro-occupy central talk in mainstream chinese-language newspapers (except for the pro-occupy Apple Daily) takes place in the sport sections. Would you have any practical suggestions, beyond the ones you gave in the main section, about features that might be different in analyzing Chinese rather than English texts? Many thanks for your very effective summary and best regards Marco

Great subject. Have you had a chance to look at this post on discourse analysis and foreign languages ( http://www.politicseastasia.com/studying/discourse-analysis-and-foreign-languages/ )? Aside from the more generic things I have tried to collect there, on this topic I would look at the structure of the text and the way papers employ vague phrases to remain ambiguous (the gritty opinions are usually packaged between intros and conclusions full of standardized phrases, and they are rarely concrete – lots of metaphors and analogies, in my experience). You could also let students look at word-groups and their connotations – particularly the nouns, considering how common noun-phrases are in Chinese. Just a few thoughts. Hope this helps – have fun with this topic! Very exciting.

Dear Florian, many thanks for the suggestions! Yes, I had a look at the post on discourse analysis and foreign languages, I just needed something more specific to get started. If enough students will be motivated to pursue this topic, we might present something at the Hong Kong Linguistics Forum this December, and have more questions in the process- I’ll keep you updated. Best Marco

' src=

Thanks for this! A great how-to guide for students! Well done!

Cheers, Todd

Thanks Todd, that’s very kind. :)

' src=

thanks for ur teachings,what if am doing a reseach on newspaper’s language?

Hi Andrew. Most of what I’ve included here can also be used on newspaper texts. If you want to read a book specifically about newspaper analysis, though, I’d check Richardson’s work: http://www.amazon.com/Analysing-Newspapers-Approach-Critical-Discourse/dp/1403935653 . Hope this helps!

' src=

Hi, can anyone help me or advise me how to answer this question please? is compute based analysis of texts and discourse a help or a hindrance? I very appreciate your time and effort. Thanks

I don’t think any methods is ever really a “hindrance”, but whether something is useful or not depends on what questions you want to answer. For qualitative issues, like the rhetorical strategies in a particular speech or publication, you probably won’t need computational approaches. But when you are examining large amounts of texts, and when you want to see how words or word categories play out quantitatively, computational methods can be a big help. I would always suggest considering a mix of methods that fits your project, for instance using computational corpus analysis to get a bird’s-eye view of your sources and then deploying qualitative methods to explore detailed examples.

' src=

can somebody tell me how can discourse analysis and sociolinguistics work together towards language power?

' src=

Hi Florian It is a great work by you, i regret i found it only now. i am working on CDA and violence against women. your post is very useful Thank you

Glad to hear it, thanks Radha.

' src=

Thnx for a very enlightening explanation. I’m thinking of analysing newspapers headlines in relation to a specific event to examine the ideology of the newspapers. I understood that CDA is in a way a must for that topic. Now I have 3 theories that I can’t make up my mind which is better: Fairclough, van Dijk and richardson. Can u advise me which one would be the best? I appreciate ur help.

Hi Dalia, thanks for the question. Do you think the three theories would have to be mutually exclusive? If you had to pick one author, I’d probably say focus on Richardson, considering your focus on newspapers. That said, Fairclough’s Marxist take, van Dijk’s strong empirical work, and Richardson’s concern about news media could potentially be connected. I would definitely mention all three in your write-up of the project, to be honest.

' src=

this will be so helpful with my course work and dissertation topic. I would need some advice on how to code my dissertation, I want to analyse UK and US newspaper to find out if their reports on Ebola in Africa were factual or was geared towards scaremongering. I would be grateful if you could contact me my email so I share with you the details and get your opinion on my work. Thank you

' src=

Beste heer,

Hartelijk bedankt voor uw artikel, ik had nooit beter kunnen vinden wat discoursanalyse betreft! U redt a.h.w een studente in nood.

Laat ik me even voorstellen: ik ben studente aan de Vrije Universiteit Brussel, departement Toegepaste Taalkunde. Dit academiejaar schrijf ik mijn bachelorproef over de openbare toespraken van Benito Mussolini. Ik beperk me tot vier toespraken, omdat ik daarvan ook en vertaling maak. Het is dan uiteraard de bedoeling dat ik hiervan ook en grondige analyse maak, die ik zonder dit artikel waarschijnlijk nooit tot een goed einde zou kunnen brengen.

Vriendelijke groeten, Tatiana

Dear Tatiana, Briefly in English: thanks for your comment. I’m very glad that these materials helped you. I’ll keep my fingers crossed that the BA thesis works out well! Sounds like a great topic. All the best Florian

' src=

Hi Dear Folrian, I would wonder if the feasibility-non-feasibility discourses of Eclectic CDA theories (frameworks/models)-blended from Fairclough, Chilton, Wodak, van Dijk, van Leeuwen, Foucault, etc. are applicable. I mean, is it applicable to use blended CDA in analysing hegemonic contestations and balance-equipoise for history texts? Alelign A.

Hi Alelign, If I understand you correctly, you are wondering whether it’s alright to mix different approaches to discourse analysis in order to figure out how domination and resistance work in history texts, right? If that’s the case, then I don’t see why not. I’m very much in favor of being eclectic. After all, what matters is the questions you have. Which specific approaches to draw from to get your answers should then always follow from those questions. Also, there is a lot that the authors you mention have in common, which means you have a rich set of sources to draw from if you want to get a handle on your topic. Best – Florian

' src=

Could you explain form me more about MEDIUM ? please.

Hi Lamia, I’m not sure I understand your question. Are you asking how to figure out whether a discourse is affected by the type of medium it is communicated in? When I use the word medium above, what I mean is the “container” or “conduit” through which a message gets communicated. Your television is a medium, as is a newspaper. One important question to keep in mind is how the things that are being communicated might rely on the specifics of the medium. If I broadcast a message on TV, I can use very different communication strategies than if I write the same message down. I’ll be able to combine sound, images, and spoken words, for example. If I use the medium of the newspaper, I can use different scripts, different headers, and the layout of the page to add meaning to the written word. So an important work step is to ask: how does a specific text use the affordances of its medium to get a point across? That’s what I have in mind above when I mention McLuhan. If you are interested in such debates about how the medium matters to the message (or how the medium might even be more important than the message), you might want to check out McLuhan’s work. I can also recommend Noel Carroll’s book “The Philosophy of Mass Art” or Friedrich Kittler’s “Gramophone, Film, Typewriter” (the latter is not easy to ready, though). I hope this answers your question. Best Florian

' src=

Dear Florian, Thank you so much for this material. It is very helpful. My background is not much of language but I plan to use Discourse to analyse my research work which is about the rhetoric of ‘Transformation Agenda’ used by my country’s party in government. The party uses it as a political programme for developing the country. Just like Obama would say ‘Change’ for instance. Just trying to figure out how my research question will sound like.

Hey Desmond, This sounds like a good starting point. In fact, the idea of change could potentially become the basis for your coding strategy: you could try to isolate the various statements that the government makes regarding change, and you could then examine in detail how the speakers/writers conceptualize “transformation”. I suspect it might be interesting to then ask how such a concept relates to views of “modernity”, particularly to ideas of “progress”, but you’ll of course have to decide what makes sense, based on your sources. All the best of success with this exciting project! Florian

' src=

dear sir, firstly I thank you so much for this fine material for students like us. secondly, i want to inquire about CDA on web newa. i want to do MA thesis on BBC website news articles about the particular case of War on terror, Women rights and politics in Pakistan. how many topics should I choose among the three?how many articles should I work? and how should i apply CDA on the selected news articles and their headlines? regards!

Hi David, This is a pretty big question. I’m not sure I can answer all of it, and I would definitely recommend you talk to your supervisor about what he or she thinks is sufficient for your specific degree requirements. I think that analyzing BBC web news would make for a good study, provided you are able to justify why you are picking the BBC (as apposed to any other major news service). Your study will, effectively, be a study of BBC reporting – which is interesting, but which won’t allow you to generalize too much (e.g. what UK news is generally like, or even what all English-language news on these subjects are like). As long as this is clear, you could potentially have a strong case here. I would, however, limit an MA thesis to one topic. Three different issues seems like a lot, and such an approach would probably be more appropriate for a PhD. I would narrow down what you are looking at and pick only one theme. I would also decide on a time frame, so that you are not swamped with articles. If you had to look at all BBC news articles on the “War on Terror”, for example, I imagine you would only be able to get a grip of the sources using quantitative methods like corpus analysis. Depending on what you want to look at, less could very well be more. At any rate, examining the headlines is surely a good start, but I would also look at the structure of the various texts, as well as detailed statements that get made on specific (sub)topics. Also, it might be good to check what images accompany the texts. Just a thought. I hope these comments are useful, even if they are admittedly rather cursory. Do make sure to check with your supervisor to clarify what makes sense for your specific project. Best Florian

' src=

Dear Florian, It is very lucky to meet you and your website when I considering to start the discourse analysis assignment. I am a Chinese student who is currently studying in UK, to be honest, I have studied and reviewed all kinds of article concerning the CDA for several days, but until now, I am still have no idea what is discourse analysis and the purpose of the analysis. I was trying to follow the steps you summaried for beginer, but I just could not decided what should I write and what contents should be included in my article. I do not know whether it is because Chinese and western mindset are different. Moreover, often I found myself could not follow and understand what the author trying to say in the English artile (even 8.0 in reading in my IELTS test). I guess it is the main reason for spending several days in reading but no determination yet. My assignment requires 3500 words, I do not worry too much if I get started, but I just do not know how to start. I guess it would be much helpful if I could read some short examples of this kinds of article. Forgive me if my wrtting cofused you, i hope you could understand my meaning. Writting in English and English thinking is really a headache for me…….Help me. Thank you. Merry Xmas and Happy New Year!

Cheers, Long

Hi Long, I sympathize with how difficult it is to get a hang of discourse analysis. It’s already hard for native speakers, but having to do all this in a foreign language is a daunting task. I don’t have a lot of good advice, other than to check what analysts have done in your own language. I can recommend the colleagues at Zhejiang University. Shi Xu, for example, has been doing some great work there, and some of the analyses he and his colleagues have published are in Chinese. Take a look at his website: http://www.shixu.com/# . I hope you’ll find what you are looking for there. In addition, you could also look at some of the journals in the CNKI database. There should be quite a lot on 话语研究. Also, on the more general side, Oxford University Press’ “Foucault – A Very Short Introduction” has been translated into Chinese. Might be worth checking out, if only to get a grasp of the basic premises. I hope this helps! All the best Florian

Dear Florian, thank you very much for all the help. I will read more articles to further deepen my understanding on discourse analysis. At the moment, I am thinking to analyze a recent XI Jinping’s speech during a national event, by using the rhetoric theory and analyze the use of ethos(do not clear about yet), pathos (use of certain “we” “Chinese people” pronouns, and words that emboies the aspiration and sympathy) and logos (argumentaiton, claims and historical data) in that speech. Is it a discourse analysis? One of the marking criteria is we should have clear theory framework and methodology, can I say the Aristotle’s rhetoric theory is my framework? I was get the idea from another english article who analyzed Obama and G. Bush’s speech, and i believe it will be safe for me to follow their method to analyze my own data. I will not copy the words, but the idea and the way of their analysis. I am not sure whether this is a cheating/plagiarism. Your help would be highly appreciated if you could give me any comments on my this tentative thoughts. thank you. Cheers.

I am also international student in the UK and I also have a discourse analysis assignment. I am really struggling with writing this essay please if you have found any good articles or advice help me please

Creating a Thesis and an Outline for a Critical Analysis Essay

LESSON Many college courses, including psychology, literature, philosophy, microbiology, and history, require large amounts of reading. Your instructor may assess your understanding and analysis To analyze is to make a thoughtful and detailed study of something. An analysis is the end result of analyzing. of a text Words that make up a book, essay, article, poem, or speech. through an exam; however, you may also be required to write an essay A short piece of writing that focuses on at least one main idea. Some essays are also focused on the author's unique point of view, making them personal or autobiographical, while others are focused on a particular literary, scientific, or political subject. that measures your understanding and opinion Point of view that shows a personal belief or bias and cannot be proven to be completely true. of a chapter or article A non-fiction, often informative writing that forms a part of a publication, such as a magazine or newspaper. . Sometimes these are assignments that ask you to assess the effectiveness of an author A person who wrote a text. 's work, or how well he or she has made a case.

Keep in mind that the idea of a work's "effectiveness" is subjective because it is based upon your opinion of the author's success. In other words, it is possible that you and a classmate or colleague might disagree about the effectiveness of a specific text Words that make up a book, essay, article, poem, or speech. . This is not uncommon; sometimes there is no "right" answer. For this reason, it is important that you thoroughly understand the text and then provide sound reasoning for your opinions.

In this lesson, you will learn how to develop a thesis statement A brief statement that identifies a writer's thoughts, opinions, or conclusions about a topic. Thesis statements bring unity to a piece of writing, giving it a focus and a purpose. You can use three questions to help form a thesis statement: What is my topic? What am I trying to say about that topic? Why is this important to me or my reader? for a critical analysis essay and how to create a corresponding outline A preliminary plan for a piece of a writing, often in the form of a list. It should include a topic, audience, purpose, thesis statement, and main and supporting points. using evidence Facts, statistics, or expert testimony that supports a claim. to support your thesis An overall argument, idea, or belief that a writer uses as the basis for a work. .

Develop a Thesis Statement

Since the purpose of a critical analysis essay is to assess the effectiveness of a text at its most basic level, your thesis statement should refer to the text that you are analyzing and express whether you think that text is effective or not.

Remember, you are looking at the extent to which a text successfully produces the outcome or result it was meant to produce. Therefore, the first step in developing your thesis statement is to identify what the author wanted to accomplish. The second step is to assess the author's success in doing so.

Here are two examples of critical analysis thesis statements covering the same text. This thesis statement affirms the effectiveness of the author's work:

In Capitalism in the Twenty-First Century , Thomas Piketty successfully argues that without government intervention, the gap between the rich and the poor will continue to grow because of an economic system that favors earnings on investments over earnings on labor.

Conversely, this thesis statement is critical of the author's effectiveness:

Thomas Picketty's book , Capitalism in the Twenty-First Century , does an excellent job of demonstrating how wealth continues to grow through investments, but fails to provide evidence that this favorable growth keeps people from moving from the lower class to the upper class through determination and hard work.

Develop an Outline

The next step to writing a critical analysis essay is to develop an outline. In addition to outlining the body, or supporting paragraphs A selection of a writing that is made up of sentences formed around one main point. Paragraphs are set apart by a new line and sometimes indentation. , you should provide a brief summary A brief restatement of an author’s main idea and major supporting details. Summaries are factual and should be written in the third-person with an objective point of view. of the text you are evaluating in the background Information that describes the history or circumstances of a topic. portion of your introduction The first paragraph of an essay. It must engage the reader, set the tone, provide background information, and present the thesis. . This will give your readers the context The larger setting in which something happens; the "big picture." they need to assess your analysis, which is especially important if they have not read the text you are evaluating.

In the supporting paragraphs, you should use the MEAL concept An acronym that describes a method of organizing the paragraphs in an essay. Under this plan, each paragraph should have a M ain point, E vidence, A nalysis, and a L ink to the next paragraph. to outline the main idea The most important or central thought of a reading selection. It also includes what the author wants the reader to understand about the topic he or she has chosen to write about. , evidence, analysis, and link To connect ideas together within a paragraph or to create a transition from one paragraph to the next, as well as back to the thesis. .

Main Idea:  your topic sentence A sentence that contains the controlling idea for an entire paragraph and is typically the first sentence of the paragraph. , identifying one of the supporting claims A statement that something is true, such as the thesis of an essay. A successful writer must present evidence to prove his/her claim. for the thesis.

Evidence:  facts A piece of information that can be proven. Something that is true and indisputable. , expert Someone who is very knowledgeable about a topic. opinion, or anecdotal evidence A brief, interesting story that supports a claim in a critical analysis or persuasion essay. proving that the claim described in the topic sentence is true.

Analysis:  explaining how the evidence supports the topic sentence.

Link:  a transition Tying two events, passages, or pieces of information together in a smooth way. In writing, transitions are sometimes called links. from the paragraph, as well as back to the thesis.

In the essay, you need to use pieces of the original text as your evidence. If you think the text is effective, identify portions of the text that demonstrate its effectiveness; likewise, if you think the text is ineffective, identify portions of the text that demonstrate its ineffectiveness. In your analysis, you will explain why each portion supports your claim that the evidence contributes to the effectiveness or ineffectiveness of the entire text.

Keep in mind that you may have more than one piece of evidence or analysis for each of your main points, so your supporting paragraphs may look like MEEAL or MEAAL, or other combinations of evidence and analysis.

Finally, you should outline your conclusion The end portion of a writing that contains a summary or synthesis of the idea in the work. This includes a recap of key points and reminders of the author's purpose and thesis statement. . In this paragraph, you need to bring all the parts of the essay together in the synthesis and create a strong final impression for the reader.

Here is what an outline for a critical analysis essay might look like:

  • Final Impression

Whether it is for a school assignment or a work task, developing the skill of outlining an essay is important. The bigger the assignment, the more important an outline becomes. Writing an outline requires you to closely examine your assignment or task and understand what is being asked of you; it also helps you organize your thoughts, stay on task, and explain your reasoning to others.

Imagine that you are working for a large hospital system, and are reviewing two different proposals for upgrading the hospital's technology system. You will need to evaluate the strengths of each proposal and report back to the larger leadership council on which proposal makes its case more effectively and should be implemented. If you do this successfully, the hospital will have a superior technology system that meets its needs. Your efforts at ensuring the success of the hospital will also make it more likely that you will be asked to take on important tasks in the future, increasing your chances for promotion.

The text below is an example of the kind of writing you might be assigned in one of your courses. Read the text and then review the sample thesis and outline of a critical analysis of the text that follows.

From "The Case for Recess" by Linda Acri in Chicago Family Weekly

Under pressure to improve student grades, many schools have cut back on recess, or even dropped it altogether. This is shortsighted and potentially dangerous, since studies show that unstructured play promotes educational, social, emotional, and creative development.

It may seem logical that more time in the classroom leads to better grades, but research suggests that recess is also important for academic success. Switching between structured and unstructured activities refreshes the brain and enhances its ability to store new information. Too much time spent on one type of task reduces the amount of information a child can absorb, while occasional breaks from schoolwork improve concentration.

The positive effects of recess go beyond grades into expanding the social and personal skills of children. Recess gives children time to talk and connect with one another, which strengthens their communication skills and puts them at ease with school and their peers. Free time at school can help children develop persistence and self-control. Creative skills are boosted when kids plan and design their own games and activities. If we want schools to help children not just learn but also grow as people, we must provide them with time each day just to be kids.

In his 2012 study "Sedentary Children are Blue, Bored, and Belligerent," Doctor Mark Phillips of the Main Hospital demonstrates that children need exercise, fresh air, sunlight, daily interaction with peer groups, and time at school during which they aren't being told what to do. Otherwise, they become "tired, bored, depressed, angry, antisocial, and unfocused." Phillips goes on to say that "schools must take responsibility for what is happening to children," and even suggests that the elimination of recess "borders on criminal."

Recess is also important because many children don't have the opportunity or inclination to play outside when the school day ends. Some participate in sedentary after-school programs like tutoring or arts and crafts. Others go right home, but stay indoors watching electronic entertainment or doing homework rather than playing tag in the yard or throwing around a ball. Many parents don't let their children roam their neighborhood the way they themselves once did. Due to both real and imagined dangers, few adults are comfortable letting their children play outside, particularly in urban neighborhoods or after dark. When I talked with one mother, she told me, "It's just not safe to let them go outside. Look at all the child abductions on television!"

We must help our children to thrive in all the ways they should. School administrators, city councilmen, and parents, think back to your childhood. Remember when you could barely sit still at your desk, filled with gleeful anticipation of schoolyard games, friend time, freedom from the stuffy classroom air, and the opportunity to rest your mind and pencil-gripping hands? Let's give kids a break. Bring back recess!

After reviewing the above text, the next step is to write a thesis statement for a critical analysis of the text. Once you have determined your thesis, you should create your responses, ideas, and thoughts to create an outline evaluating the text.

Thesis: Linda Acri's "A Case for Recess" successfully makes a convincing and persuasive argument for why we must fight for our children's recess time.

Outline evaluating text:

  • Hook: While having tablets, electronic chalkboards, and more intense learning environments in schools might thrill some parents, there really is no substitute for permitting children to play like children.
  • Background: Acri's article sets forth the current problem of schools cutting back student recess time and the importance of recess in a child's educational, psychological, emotional, and intellectual development and overall life.
  • Thesis: Linda Acri's article, "A Case for Recess," successfully makes a convincing and persuasive argument for why we must fight for our children's recess time.
  • Evidence: Acri points out that "switching between structured and unstructured activities refreshes the brain and enhances its ability to store new information."
  • Evidence: Acri cites a study by Doctor Mark Phillips that describes how children become "tired, bored, depressed, angry, antisocial, and unfocused" without recess.
  • Analysis: The argument is effective because it takes a popular idea and refutes it with strong evidence.
  • Link: Further, the evidence suggests that the benefits go beyond schoolwork.
  • Evidence: Recess develops skills such as communication, persistence, and self control that not only improve academic achievement but also help children improve their social, emotional, and creative skills.
  • Analysis: This argument is powerful because it shows that eliminating recess harms not just grades but personal growth.
  • Link: More and more, society expects schools to not only teach but also to help raise children: in order to help children learn life skills we must provide them with time each day just to be kids.
  • Evidence: Many children don't have the opportunity to play outside after school.
  • Evidence: One mother told Acri, "It’s just not safe to let them go outside. Look at all the child abductions on television!"
  • Analysis: Her argument appeals to many readers because it includes a number of scenarios, at least one of which is probably relevant to almost everyone.
  • Link: She shines a spotlight on the fact that most children are not able to enjoy the freedom to play and explore the way their parents did.
  • Synthesis: Acri's argument about the importance of recess in nearly all areas of child development and happy living convinces the reader to fight for unstructured play time in schools.
  • Final Impression: We must encourage schools to recognize the needs of children to exercise, socialize, and rest their brains, and to once again see recess as a benefit rather than a hindrance to academic progress.

Read the text below. After reading it, write an appropriate thesis statement for an essay evaluating the text, followed by an outline of this evaluation.

From "Employers Violate Civil Liberties Over Online Videos and Posts" by Lionel Burnett; Opinion Section, New York Weekly Post

If you aren't hooked up online then you might as well be nonexistent. Your online presence is basically who you are today. It's a fundamental right to be who and what you want to be online as much as it is in "real" life.

Social media has really changed how people relate to one another. We don't have to see people face-to face anymore. We can work long hours or live far apart and still keep up with the life events, celebrations, trials, and tribulations of friends and family. With a couple swipes of the finger on a tablet, I can find out who your friends are, where you go to school, who you work for, and what music you listen to. I can even find out what world city you should live in or what type of animal best describes your personality from the quizzes you post! Through our profiles—the photos, comments, and stories we post—we get to decide how the world sees us. It's a lot of fun! But sadly, opening our lives to the world can also cause us big, big trouble.

My friend Aaron was a teacher at a local school. He's also a guy who loves hunting. He stopped talking to people at work about his hobby after his boss took him aside and said that it was "inappropriate to discuss such matters in this environment, particularly given recent incidents. We don't want to scare the children or parents." Then last week, Aaron posted a few pictures of his latest hunting trip online, along with a video of him showing his eleven-year-old son how to properly load, fire, and unload a shotgun. All his friends thought that it was awesome that he spent time with his son while teaching him gun safety. But then the video went viral, and the principal and superintendent at Aaron's school heard about it. They called him in, and they fired him! They said he'd been warned, and that posting the video was irresponsible. Aaron was fired even though he never signed a contract or committed to any guidelines around using social media. It isn't right and it isn't fair.

Not long ago I had to sign a "social responsibility" statement for my job. The contract requires employees to review the policies and standards of the organization and exercise good judgment online. Human Resources has also issued a ludicrous one-strike rule. This new policy states that if we post something that reflects poorly on the industry, the company, or any employees, we must either a) deactivate our online accounts or b) change our profile names so no one will know where we work. If we refuse, we will be fired. This is a violation of civil liberties! No piece of paper I am forced to sign is going to change what I choose to do online.

No company has the right to tell an employee how to behave in his or her personal life. I fail to see why our Internet lives should be any different than real life. My boss goes out partying every night, but he didn't have to sign a contract saying he would watch what he says or does in a bar. If he tries to fire me for posting things online, I will see to it that he gets dismissed for being so irresponsible and partying all night. Of all of the employees, I guess I am the most upset about this. All of my coworkers signed the new contract without complaining. They aren't all that interested in talking to Human Resources with me either. I will serve as the lone advocate for this important cause without them. I will see to it that these companies stop violating our civil liberties by limiting our vital online presence! 

Sample Answer

While entertaining, Lionel Burnett's "Employers Violate Civil Liberties Over Online Videos and Posts" fails to successfully argue that employer requests for decent online behavior from employees is a violation of civil liberties.

  • Hook: One-sided and filled with biases, Burnett's article, "Employers Violate Civil Liberties Over Online Videos and Posts," reads more like an ill-informed rant than a newsworthy opinion piece.
  • Background: This newspaper article is an opinion piece regarding the importance of social media in today's culture and how employer involvement and concern over employee online activity is unfair and unlawful.
  • Thesis: While entertaining, Lionel Burnett's "Employers Violate Civil Liberties Over Online Videos and Posts" fails to successfully argue that employer requests for decent online behavior from employees are a violation of civil liberties.
  • Evidence: Burnett claims that interpersonal activities are no longer necessary because we can find out everything we want or need to know about everyone online.
  • Analysis: Burnett fails to take into account that many people are not active online and still value meeting their family and friends in person.
  • Link: Focusing only on his own positive views of social media, Burnett blatantly ignores that what people post online has the potential to harm others.
  • Evidence: Burnett is shocked that Aaron was fired from his position as a schoolteacher after sharing information involving guns, even though he had been warned about doing so.
  • Analysis: The school has an interest in seeing that its employees do not post online material that may reflect poorly on its staff and upset parents.
  • Link: Similarly, Burnett's employer is not acting unlawfully by requesting that its employees be mindful about their online practices.
  • Evidence: Human Resources asked its employees to sign a social media contract and although Burnett claims to be vehemently opposed, he went along with it.
  • Analysis: If Burnett is such an advocate for online legal freedoms, why did he sign the social media contract rather than finding a new job with a company that doesn't require such a contract?
  • Link: While Burnett makes an interesting point about his boss—that company expectations regarding online and offline social behaviors are not consistent—he offers no real evidence to support his argument that his employers, or Aaron's for that matter, acted unlawfully (or even unreasonably).
  • Synthesis: Although Burnett's commentary highlights some trends in social interactions in this country, i.e., a moving away from live interpersonal contact to a more virtual reality, it falls short of supporting his claim that employers violate civil liberties over online videos and posts.
  • Final Impression: Burnett offers no facts, just his opinion and personal outrage, on what's becoming a common human resources requirement: the social media contract.

Developing an outline helps me organize my ideas before I get started on writing my first draft. This saves me time and energy, keeping the first draft more focused than if I just start writing without any plan.

You need to reference portions of the text to demonstrate its effectiveness or lack of effectiveness because summaries, paraphrases, and quotes from the text illustrate the writer's actual arguments. The specific words and ideas of the writer are what your arguments and reasoning around the essay's effectiveness are based upon. Referencing the text provides evidence to support your own writing and also provides your own reader with the original text to go back and review.

Copyright ©2022 The NROC Project

Critical Discourse Analysis Compare & Contrast Essay

  • To find inspiration for your paper and overcome writer’s block
  • As a source of information (ensure proper referencing)
  • As a template for you assignment

Introduction

Defining discourse analysis, defining critical discourse analysis, the difference between critical discourse analysis and discourse analysis.

Language may be used in different contexts and texts to create different meanings. To understand the sign, vocal, or even written language, a form of analysis is crucial in establishing both the intended and implied meanings.

This paper discusses discourse examination and critical discourse analysis (CDA) as two important approaches to analysing language use in vocal, sign, and written forms. Its main concern is to demonstrate the difference between the perspectives of language use in written and text forms.

Discourse analysis (DA) is a general term that is applied to various paradigms that are deployed in the study of the sign, vocal, written, and any other language semiotics. Objects that are used in the analysis under this approach are defined in terms of an individual’s consistency in the application of prepositions, use of sentences, tongue, and even turns-at-talk (Ross & Nightingale 2003).

Opposed to traditional approaches to linguistic analysis, discourse analysis focuses on studying not only the usage of language outside the limits of sentences use, but also analysing language in its conventional usage, rather than utilising invented examples.

This claim suggests a close relationship between discourse analysis and text analysis. However, the two concepts are different since discourse analysis also objects to identify various socio-psychological traits of people, rather than just the structure of the texts (Keller 2011).

As Bryman (2008) confirms, discourse analysis may find application in various social sciences among them being linguistics, social work, cultural studies, and communications disciplines. However, in each of the disciplines, its application is subject to assumptions, methodologies of studies, and analysis approaches that guide it. Discourse analysis covers a variety of topics that are of interest to different analysts.

They include sounds, language syntax, rhetoric, meanings, gesture, interaction, and acts of speech among others. It can take different genres, including business, politics, and science among others. Discourse analysts are interested in topics such as the relationship between context and texts, discourse and power, and interaction and the discourse.

From the above discussions, the term discourse analysis simply means studies on different ways in which languages are deployed in different texts and contexts.

In a more interactive definition, as Blommaert (2005, p.97) informs, ‘it concerns itself with the use of language in a running discourse, continued over a number of sentences, and involving the interaction of a speaker, writer, auditor, or a reader in a specific situational context, and within a framework of social cultural conventions’. Indeed, it is not just concerned with the methodology.

Its studies include the nature of usage of language and its relationship with key issues that scholars encounter in social science studies (Ritchie & Lewis 2003).

In particular, discourse analysis relates to the gathering of different perspectives of discourse. Such approaches relate to both data collection practices and theoretical assumptions together with meta-theoretical postulations that guide research approaches (Wood & Kroger 2000).

Discourse analysis differs from the grammatical analysis. Grammatical data involves one sentence or a collection of many sentences that demonstrate a given aspect of the language under study. In the process of analysis, a grammatical analyst will compile different sentences that he or she deploys as examples.

This approach differs from discourse analysis. Its primary interest is on the morphological productivity of different people as opposed to the forecaster. Discourse analysis data is adopted from recordings or written texts. Such data is hardly derived from one sentence.

Discourse analysis interconnects with rhetoric studies. Indeed, Eisenhart and Barbara (2008) reveal how discourse researches are interrelated classes of oratory, symphony, and practical morphology. Studies on speech making have been expanding. They comprise rhetoric of politics, popular culture, and informal arguments. A new pedagogy has been established concerning personal identity rhetoric.

These changes call for the expansion traditional approaches to language analysis and talks and texts in new mechanisms that reflect material and socio-cultural discourse contexts. This observation suggests that the discipline of rhetoric studies is now closely interlinked with discourse analysis.

Consequently, as Gee (2005) reveals, discourse analysis is a means of engaging in an incredibly crucial human task, which entails thinking deeply on meanings that are attached to words that people utter for the world to become a humane living place.

Critical discourse analysis is a sub-discipline of discourse analysis. It approaches discourses from a political motive. Conversely compared to campaigners and or politicians, decisive dissertation examination extends past grave matters. Analysts in critical discourses have a structural understanding and knowledge, which supersedes general insights on politically motivated issues (Renkema 2004).

They examine basic sources, the circumstances, and even the consequences of different concerns. Hence, as opposed to political scientists, critical discourse analysts have an interest in arriving at a scholarly sound contribution, which includes an in-depth insight into specific pressing politicised issues in the society.

The critical dissertation is perhaps the hardest test that discourse forecasters encounter. It demands a multidisciplinary understanding together with intricate understanding of relationships that occur in texts, power, culture, talks, and even the society. Indeed, its criterion for adequacy does not merely depend on descriptive, explanatory, or observational skills (Renkema 2004).

Success in the critical discourse analysis rests on the platform of the relevance and effectiveness of the contribution of analysis in creating change.

This situation requires modesty. Indeed, under critical discourses, educational involvement may be trivial in times of transformation, particularly if individuals who are closely engaged with reference to their conduct are successful transformation agents. This position is perhaps well evidenced by the transformation procedures that involve liberalisation, feminist campaigns, the battle for public privileges, and class campaigns.

One of the most significant concerns of critical discourse analysis involves developing an understanding of the relationship between languages, dominance, and social power. Such an understanding helps in predicting the contribution of discourse on the reproduction of various power differences.

While discussing social power, critical discourse analysis ignores powers that individuals portray, unless under circumstances in which the powers contribute to the development of productive relationships between different social groups.

Social power may be manifested in the form of accessibility to various valuable resources in social platforms, including wealth, education, skills, knowledge, and even status. Under critical discourse analysis, accessibility to different forms of power from the context of communication and discourse is a crucial resource of power.

In critical discourse analysis, political motive forms its basic tenet, which involves power struggles. Authority is a means of being in charge of one assembly of people over members of another assembly. It limits people’s cognition and actions. Hence, it influences people’s minds and their freedom of action.

Power is enacted through acts of persuasion, manipulation through talks, and dissimulation. The goal is to alter people’s cognitions and thinking processes in an effort to align them with those of the influential social groups. To this extent, critical discourse analysis helps in the management of other people’s minds via texts and talks.

Critical discourse analysts garner different topics that require analysis before proceeding to collect large amounts of texts. However, the corpus of texts does not comprise the only methodology for critical discourse analysis.

Different researchers grant the right to apply all methods that permit the generation of insights to ideologies that the discourses promote. The critical discourse analysis investigates different text echelons that range from micro, macro, and meso levels of text to identify political motives in them.

Critical discourse analysis involves the utilisation of different techniques of studying language and texts as a cultural and social practice (Seale 2004). It draws its tenets from the poststructuralist pedagogy that investigates the functions of all institutional sites.

It also contends that language and texts play important roles in the development of human ideologies and identities (Eisenhart & Barbara 2008). Similar to the concerns of Bourdieu’s sociology, critical discourse analysis holds that texts and/or interactions with them utilise embodied approaches that operate in different social fields (Seale 2004).

Critical discourse also draws some of its facets from the ideologies of the neo-Marxist theory of culture, which assumes that discourses are created and utilised in the political economy. This observation perhaps explains its particular focus on political motives. Thus, it is different from discourse analysis to the extent that it has specific areas of interest.

Discourse analysis focuses on a variety of genres, including phonology, pragmatics, communication ethnography, conversation analysis, critical discourse analysis, rhetoric, text linguistics, and functional grammar amongst others (Bromley 2001; Crang & Cook 2007). Hence, critical discourse analysis is a genre of discourse analysis.

Considering that discourse analysis has a variety of genres, including critical discourse analysis, the difference between the two concepts is clear with reference to the structures and the main concerns of the critical discourse analysis. Practice techniques that are deployed in critical discourse analysis are borrowed from interdisciplinary fields.

For example, just like in the case of pragmatics, the theory of speech acts, and narratology, which are advanced in discourse analysis, critical discourse analysis holds that texts comprise a complex mechanism for social actions, which take place in sophisticated contexts on a social platform (Gee 2005).

Functional linguistics studies depict the manner in which language forms can relate to achieve ideological functions. The theory is used in the critical discourse analysis as an analytic tool for establishing the relations between culture, politics, gender, and social classes.

Critical discourse analysis acknowledges the existence of asymmetries in resources and power among different speakers, including people who listen to them. It holds that writers and readers have unequal accessibility to social and linguistic resources, a situation that reveals their differences in social contexts.

From the paradigms of discourse analysis, discussion combines with languages to influence the ideologies of people’s daily affairs and hence the asymmetry that is evident between textual portrayals and the relations of power. The CDA is both constructive and deconstructive.

From the paradigms of power and textual portrayals, in a deconstructive approach, CDA renders power relationship themes problematic in a society as expressed through written texts and talks. In a constructive approach, it advocates the increased development of critical skills that are necessary for the analysis of discourses and various social relations to ensure equity in terms of resource distribution (Keller 2011).

Discourse analysis deploys text as its main unit of analysis. This approach differs from discourse analysis, which can use a sound and its patterns, textual frameworks, and rhetoric in the analysis. CDA considers texts social actions that form meaningful and reasoned printed and verbal language.

However, it does not consider textual forms random in nature. Specific types of texts do certain things within various social institutions. They can help to predict material effects in qualitative researches (Denzin & Lincoln 2005). Under critical discourses, studies are dynamic. They continue undergoing processes of reinvention and innovations.

From the paradigms of discourse analysis, all genres can be adequately analysed via studying language structures such as prepositions and microstructures of the texts. The discourse reveals how written and verbal languages possess various identifiable segments and movements. For example, a scientific text can be interpreted as a series of actions that have been joined by a set of chains.

CDA can focus on word-level and sentence-level analysis. It does this by using analysis approaches derived from functional linguistics studies. Halliday and Matthiessen (2004) support this assertion by adding that grammatical combined with various lexical textual features possess different identifiable functionalities. They can explain the natural and social world.

They create different effects on social relations through the conventions that form coherency in texts that are deployed in a given media. Critical discourse analysis focuses on identifying these effects.

Under discourse examination, verbal texts depict some chosen perspectives of the natural world to help in explaining the social world. Therefore, through texts, people can position others to align with identifiable relations that are consistent with power differences that exist among them.

Language employment in writing and speech (discourse) constitutes a social practice. Discursive events shape social structures and/or institutions. This claim suggests that discourse is socially conditioned and that may be socially constitutive. Since it reproduces the status quo, it contributes to its transformation.

Discursive events also play the role of reproducing varying power relations among different classes of people in the society (Fairclough 2000).

Depending on the academic culture under investigation, different linguistic scholars can use the term discourse in different contexts. For example, German linguistic scholars distinguish discourses from texts, depending on the relationship between traditional text linguistics and language rhetoric. However, in English-speaking nations, discourses imply oral communication and written texts.

It is possible to consider a transcript a tangible comprehension of conceptual structures of knowledge. However, amid these differences, discourses encompass a form of memory and knowledge bases that are manifested in the form of power differences that are witnessed in talks and in written texts (Reisigl & Wodak 2001). However, critical discourse analysis focuses on structures of talks and written texts.

Dominance reproduction is a major aim of CDA. Dominance has reception together with reproduction as two important perspectives in its contribution to power differences. This observation suggests that CDA analysis focuses on the legitimisation and the expression of dominance in different structures of talks and texts.

Reproduction of various discursive events in CDA emanates from power differences that are manifested in the form of social cognition power among some groups over others. As studied from the paradigms of discourse analysis, discourse structures translate into social cognitions while social cognitions in CDA produce power imbalances.

Therefore, under the two approaches, researchers struggle with establishing the relationships between cognition and the discourses. However, under both CDA and discourse analysis, discourse structures play the role of mediation. Thus, they are mechanisms for reproducing dominance in written texts and speeches.

In the context of dominance, CDA differs from DA in its emphasis on power variations among different groups of people who interact in social contexts through talk and written texts. To this extent, Fairclough (2000, p.103) reckon, ‘members of less powerful groups may also otherwise be more or less dominated in discourse’. This claim implies that in all levels of talks and texts, participants who possess influential power control freedom.

Consequently, in CDA, language does not possess any power of its own. It acquires it when it interacts with high-ranking people. This observation perhaps reveals why CDA conducts the analysis of discourse from the perspective of distinguished people. Such people carry the load when it comes to inequality issues. They solely have the ability to improve social conditions.

Discourse, which denotes language use in talk and written texts, can be studied from the paradigm of discourse analysis (DA) and critical discourse analysis (CDA).

Discourse analysis constitutes a variety of genres such as phonology, pragmatics, and critical discourse analysis among many other genres that study language use in social contexts. CDA is a genre of DA. CDA focuses on political motives in language use, which is manifested through power differences that create the dominance of different groups in social communication contexts.

Blommaert, J 2005, Discourse , Cambridge University Press, Cambridge.

Bromley, D 2001, Toward reflexive ethnography , JAI, London.

Bryman, A 2008, Social research methods, Oxford University Press, Oxford.

Crang, M & Cook, I 2007, Doing ethnographies, Sage, London.

Denzin, N & Lincoln, Y 2005, The SAGE handbook of qualitative research, Sage, London.

Eisenhart, C & Barbara, J 2008, Discourse Analysis and Rhetorical Studies: Rhetoric in Detail: Discourse Analyses of Rhetorical Talk and Text , John Benjamins Publishers, New York, NY.

Fairclough, N 2000, The discourse of social exclusion: Approaches in Critical Discourse Analysis , Passagen Verlag, Vienna.

Gee, J 2005, An Introduction to Discourse Analysis , Routledge, London.

Halliday, M & Matthiessen, C 2004, An Introduction to Functional Grammar , Arnold, London.

Keller, R 2011, ‘The Sociology of Knowledge Approach to Discourse (SKAD)’, Human Studies, vol. 34, no. 1, pp. 43-65.

Reisigl, M & Wodak, R 2001, Discourse and Discrimination , Routledge, London.

Renkema, J 2004, Introduction to Discourse Studies , Benjamins, Amsterdam.

Ritchie, J & Lewis, J 2003, Qualitative research practice: A guide for social science students and researchers, Sage, London.

Ross, K & Nightingale, V 2003, Media and Audiences New Perspectives , Open University Press, Virginia.

Seale, C 2004, Social research methods: a reader , Routledge, London.

Silverman, D 2005, Doing qualitative research: a practical handbook, Sage, London.

Wood, L & Kroger, R 2000, Doing Discourse Analysis , Sage Publishers, London.

  • The Internet and Freedom of Speech: Ethics and Restrictions
  • Colgate Brand's Promotional Strategy
  • English Literature Impact on Racism Among Africans
  • Problem of Racial Slurs in Australia
  • "Immigrant Workers Cleaning and Caring in the Shadows of Affluence" by Pierrette Hondagneu-Sotelo
  • Women in Asian Literature in The Tale of Kieu by Du Nguyen and the play The love suicide at Amijima
  • Magical Realism: Garcia Marquez
  • Bamako: Movie Concept and Theme
  • Chicago (A-D)
  • Chicago (N-B)

IvyPanda. (2019, June 23). Critical Discourse Analysis. https://ivypanda.com/essays/critical-discourse-analysis/

"Critical Discourse Analysis." IvyPanda , 23 June 2019, ivypanda.com/essays/critical-discourse-analysis/.

IvyPanda . (2019) 'Critical Discourse Analysis'. 23 June.

IvyPanda . 2019. "Critical Discourse Analysis." June 23, 2019. https://ivypanda.com/essays/critical-discourse-analysis/.

1. IvyPanda . "Critical Discourse Analysis." June 23, 2019. https://ivypanda.com/essays/critical-discourse-analysis/.

Bibliography

IvyPanda . "Critical Discourse Analysis." June 23, 2019. https://ivypanda.com/essays/critical-discourse-analysis/.

Recommended pages

  • Undergraduate open days
  • Postgraduate open days
  • Accommodation
  • Information for teachers
  • Maps and directions
  • Sport and fitness

Written discourse

Essays marked with a * received a distinction.

  • * Analyzing and raising students’ awareness of textual patterns in authentic texts : Mohammad Umar Farooq
  • Written Text Analysis : Gregory S. Hadley
  • *Show an analysis of the whole text in terms of the main underlying text pattern. Identify the signals that indicate this pattern David Evans
  • Critical discourse analysis: A letter to expatriate from the Rt. Hon. Sir Norman Fowler MP : Andrew Atkins
  • * Teaching English Textual Patterns to Japanese Students : Michiko Kasuya
  • * A analysis of a Korean student’s written English text : Yvette Murdoch
  • A text analysis of 'Taking Failure by the Throat' : Marian Dawson
  • Problems in processing text produced by a student : Alan Macedo
  • Applying written discourse analysis in a Japanese EFL class :  Cindy Cunningham
  • Referential discourse structures and the creation of text: an analysis of student writing samples : William Penny
  • How to get away with things with words: An Examination of Written Texts : Jeremy Scott Boston
  • A Text analysis of a newspaper article about Konglish taken from ‘The Korea Heral d' David Doms
  • * Increasing comprehension and production of cohesion through conjunction : Thomas Warren-Price
  • * An Evaluation of American Headway 3 Mary Umemoto
  • Choose an authentic text in English. Analyze the text in terms of problem-solution, general specific or claim-counterclaim patterns.  Briefly discuss the challenges and opportunities that such text patterns present for teachers of English as a foreign language . Andrew Rolnick
  • * The Use of Critical Discourse Analysis with Korean Adult Learners , Terry Faulkner   
  • Do Students Need Critical Discourse Awareness? H. Douglas Sewell
  • * Paraphrasing: An Introductory Unit In Paraphrasing in Academic Discourse   Deborah Novakova
  • * The Value of Enhancing Students’ Critical Awareness of Discourse Philip Shigeo Brown
  • * Science or Slaughter? Two Opposing Views on Japanese Whaling: a Critical Discourse Analysis Jason Peppard
  • The Findings of Written Discourse Analysis and how they are Articulated in Learning English for Academic Purposes   Sandee Thompson
  • * Two Views, Two Discourses: A Critical Analysis of how Ideology is Interpreted and Reinforced through Opinion Articles Michael Chang
  • On Analysing a Problem-Solution Text Pattern Fernando Oliveira
  • How to Raise Awareness of Textual Patterns Using an Authentic Text   Seiko Matsubara
  • * The Politicisation of Death, Methods of Embedding Ideology within the News: A Critical Discourse Analysis of Two News Articles Michael Post
  • * Genre analysis of the 'simple joke' (with TESL/TEFL applications) , Robert Murphy
  • * Encouraging Problem-Solution Patterning and Co-Textual Referencing in L2 Written Discourse , Steven James Kurowski
  • * Japanese Revisionists and the 'Comfort Women' Issue: A Comparison of Two Texts , Michael Cooper
  • * 'One-on-One With Obama': An Analysis , Andrew Lawson
  • * Genre Analysis of a Job Rejection Letter , Garcia Chambers
  • Ideological Variations in the Representation of Hugo Chavez as a Democratic Leader in Two Different Cultures: A Critical Discourse Analysis , Parker Rader
  • A Chinese Student's Text Analysis , Soti Vogli
  • * Pedagogic applications of the Problem-solution pattern , Benet Vincent
  • * Differing Opinions: A Critical Discourse Analysis of Two Articles Stefan Thomson
  • * Trends in EBP: A Comparison of Market Leader's Writing Tasks to Findings in Written Discourse Joshua Durey
  • Sexual Bias in Institutionalised Forms of Discourse Baljinder Gosal
  • From Surface to W ider Context: Two Text Types Analysed , Sirkku Carey
  • * Trends in EBP: a Comparison of 'Market Leader''s Writing Tasks to Findings in Written Discourse Joshua Drury
  • An Analysis of Two Newspaper Articles in the Aftermath of the 2011 Japanese Tsuna mi Bruce Hope
  • An Analysis of a Mexican EFL Tex tbook: A Written Discourse Perspective Elsa Fernanda Gonzalez
  • * Korean News vs International News: A Critical Analysis of Two News Reports on North Korea Jonas Robertson
  • Immigration Articles in Two Newspapers - A Multimodal Discourse  Dominic Castello
  • Gender Relations in Institutionalized Discourses Mehboobkhan Ismail
  • *  Critical Discourse Analysis: How the Washington Post and Moscow Times Reported the Russian Airstrikes in Syria   Laurie Knox
  • * Critical Discourse Analysis of How Two Newspapers Reported the Treatment of Women at a Sumo Event in Japan Christine Pemberton

Home — Essay Samples — Sociology — Individual and Society — Discourse Community

one px

Essays on Discourse Community

Engaging discourse community essay prompts.

Finding the right prompt can set the stage for an insightful essay. Here are some thought-provoking prompts to get you started:

  • Evaluate the discourse within an online forum dedicated to sustainability.
  • Analyze the communication patterns of a professional esports team.
  • Investigate how a local art collective uses language to build community.

Picking a Standout Discourse Community Essay Topic

Choosing a compelling topic is crucial. Here’s how to make sure you land on something engaging:

  • Interest : Opt for a community you’re personally interested in or curious about.
  • Originality : Seek out topics that aren’t overdone. The more unique, the better.
  • Accessibility : Make sure you can access enough information and resources for your essay.

Examples of Discourse Community Essay Topics

To avoid the usual suspects and spark your imagination, consider these unique essay topics:

  • Discourse practices in online coding bootcamps.
  • Language and identity in expatriate communities.
  • How DIY forums challenge traditional expertise.
  • Discourse dynamics in feminist activist groups.
  • The role of language in local food cooperatives.
  • Communication styles within virtual reality spaces.
  • Analysis of discourse in mental health support groups.
  • Language use in underground music communities.
  • How digital nomads create community through discourse.
  • Discourse among members of a city council.
  • Cross-cultural communication in international business teams.
  • Language and power in academic departments.
  • Communication strategies in environmental advocacy groups.
  • Discourse in online platforms for language learning.
  • Community building in co-living spaces.
  • Discourse strategies in political campaigning.
  • Role of language in crafting a makerspace identity.
  • Online forums as spaces for medical discourse.
  • Language evolution in multiplayer online games.
  • Building a discourse community in coworking spaces.

Inspiration for Your Discourse Community Essay

Need a nudge to get your writing process started? Let these ideas inspire you:

"Exploring the esports team's communication reveals a complex system of language, symbols, and rituals, highlighting the nuanced ways members create a sense of belonging and identity."

"The vibrant discourse within the feminist activist group not only challenges societal norms but also fosters a strong sense of community and shared purpose among its members."

Understanding and Examples of a Discourse Community

"discourse community" by john swales: summary, made-to-order essay as fast as you need it.

Each essay is customized to cater to your unique preferences

+ experts online

Discourse Community

My experiences in volleyball discourse community, analysis of a discourse community: craft and diy enthusiasts, the game super smash bros. melee's community as an example of a discourse community, let us write you an essay from scratch.

  • 450+ experts on 30 subjects ready to help
  • Custom essay delivered in as few as 3 hours

The Discourse Community of Hypebeasts

Six characteristics that can be used to identify what qualifies as a discourse community, a reflection paper on football discourse community, research on discourse community of a small start-up business, get a personalized essay in under 3 hours.

Expert-written essays crafted with your exact needs in mind

Authority Within a Discourse Community: Personal Reflection

Rhetorical methods in the finance and economics discourse communities, critical discourse analysis and power relations, critical discourse analysis of race and racism, critical discourse analysis: historical origins, analyzing gender stereotypes, and empowerment in the always campaign, gender diffrences in political discourse, discourse community practices.

A discourse community refers to a collective of individuals who possess a shared set of discourses, encompassing fundamental values, assumptions, and modes of communication that revolve around common objectives.

A discourse community exhibits distinct characteristics that define its identity and functioning. Firstly, they have a common goal or purpose that unites members and serves as a focal point for their interactions. This shared objective creates a sense of belonging and facilitates effective communication within the community. Secondly, discourse communities have specific language and communication practices unique to their group. These can include specialized terminology, jargon, or even non-verbal cues that enable efficient and meaningful communication among members. Mastery of this shared language is crucial for individuals to participate actively and contribute to the community's discourse. Thirdly, discourse communities often possess established conventions, norms, and expectations regarding appropriate behavior, ethics, and standards of communication. These guidelines ensure cohesion, cooperation, and mutual respect among members. Lastly, discourse communities may have gatekeepers who regulate access and maintain the integrity of the community. These gatekeepers may be experts, mentors, or long-standing members who ensure that new participants meet the community's requirements and contribute positively to its ongoing discourse.

The concept of discourse community emerged as a framework in the field of sociolinguistics and discourse analysis. Although there is no specific historical origin attributed to it, the study of discourse communities can be traced back to the works of scholars such as John Swales and James Gee in the late 20th century. John Swales, a prominent linguist, introduced the term "discourse community" in his influential book "Genre Analysis: English in Academic and Research Settings" published in 1990. Swales emphasized the importance of understanding the communicative practices and conventions within specific communities to effectively participate in their discourse. James Gee, another influential scholar, expanded the concept of discourse community and introduced the idea of "situated learning" in his book "Social Linguistics and Literacies: Ideology in Discourses" published in 1996. Gee explored how discourse communities shape identity, knowledge acquisition, and socialization processes. Since then, the study of discourse communities has gained prominence in various fields, including linguistics, communication studies, and sociology.

1. Professional Discourse Communities. 2. Academic Discourse Communities. 3. Hobbyist Discourse Communities. 4. Cultural Discourse Communities. 5. Online Discourse Communities:

Academic Discourse Community: Scholars, researchers, and students within a specific discipline form an academic discourse community. They share specialized knowledge, use discipline-specific terminology, and engage in scholarly writing and discussions. Online Gaming Community: Gamers who participate in online multiplayer games create a discourse community. They use game-specific jargon, communicate through forums or chat platforms, and share strategies and experiences related to gaming. Professional Discourse Community: Professions such as medicine, law, or engineering have their own discourse communities. Professionals within these fields communicate using technical terminology, share professional experiences, and adhere to specific codes of conduct. Sports Fan Community: Fans of a particular sports team or sport create a discourse community. They engage in discussions, debates, and analyses of games and players, often using sports-related slang and terms. Social Media Community: Users of social media platforms form discourse communities based on shared interests, such as fashion, food, or photography. They communicate through hashtags, comments, and posts, creating a unique community around their shared topics.

Social Construction of Reality, Situated Learning Theory, Communities of Practice, Genre Theory.

The study of discourse communities holds significant importance as it sheds light on the intricate ways in which individuals and groups interact, communicate, and form shared understandings within specific contexts. Understanding discourse communities allows us to recognize and appreciate the diversity of social groups and their unique discursive practices, values, and goals. Exploring discourse communities helps us comprehend how language shapes social interactions, knowledge construction, and the formation of identities. It allows us to identify the power dynamics and hierarchies that exist within these communities and how they influence individuals' access to resources and opportunities for participation. Moreover, discourse communities play a crucial role in the transmission and dissemination of knowledge, expertise, and cultural practices. By studying discourse communities, we gain insights into how knowledge is constructed, shared, and preserved within specific fields or domains.

The topic of discourse communities is a compelling subject for an essay due to its relevance and wide-ranging implications in various fields of study. By delving into discourse communities, one can explore the intricate ways in which language, communication, and social interaction shape our understanding of the world. Writing an essay on discourse communities allows for an in-depth examination of how different communities form, develop shared understandings, and create meaning through their discursive practices. It offers an opportunity to analyze the power dynamics, norms, and values that influence communication within specific groups. Furthermore, studying discourse communities provides insights into knowledge transmission, expertise, and identity formation. It allows for a critical exploration of the role of language in shaping social relationships, access to resources, and opportunities for participation within specific communities.

1. Gee, J. P. (1996). Social linguistics and literacies: Ideology in discourses. Routledge. 2. Swales, J. M. (1990). Genre analysis: English in academic and research settings. Cambridge University Press. 3. Johns, A. M. (1997). Text, role, and context: Developing academic literacies. Cambridge University Press. 4. Prior, P. (1998). Writing/disciplinarity: A sociohistoric account of literate activity in the academy. Routledge. 5. Bazerman, C. (2004). Speech acts, genres, and activity systems: How texts organize activity and people. Routledge. 6. Miller, C. R. (1984). Genre as social action. Quarterly Journal of Speech, 70(2), 151-167. 7. Bhatia, V. K. (2004). Worlds of written discourse: A genre-based view. Continuum. 8. Barton, D., & Hamilton, M. (1998). Local literacies: Reading and writing in one community. Routledge. 9. Paltridge, B. (2001). Genre, frames and writing in research settings. John Benjamins Publishing. 10. Johns, A. M. (2017). Discourse communities and communities of practice. In T. Johnstone (Ed.), The Cambridge handbook of discourse studies (pp. 257-273). Cambridge University Press.

Relevant topics

  • Social Justice
  • Media Analysis
  • Sociological Imagination
  • Effects of Social Media
  • American Identity
  • Social Media
  • Cultural Appropriation
  • Sex, Gender and Sexuality

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

  • Instructions Followed To The Letter
  • Deadlines Met At Every Stage
  • Unique And Plagiarism Free

how to write a discourse analysis essay

University of Notre Dame

Fresh Writing

A publication of the University Writing Program

  • Home ›
  • Essays ›

Printing a New Age of Design: A Rhetorical Analysis of Neri Oxman’s TED Talk: “Design at the Intersection of Technology and Biology”

By Jason Piechota

Published: June 06, 2024

a tree growing through an opening with curved stairs wrapping around it

Could scientists 3D print a human? What about a finger? As the ability to additively manufacture and 3D print components became easier and cheaper with the 2000s, designers looked to additive manufacturing as a potential way to approach new design problems. One designer, Neri Oxaman, details her work with additive manufacturing within a biological framework in her 2015 TED Talk, “Design at the Intersection of Technology and Biology.” In the talk, Neri Oxman’s use of comparison between current design and her work establishes a base for her argument that her technology is more sustainable than current design. Oxman then utilizes different rhetorical devices to create a presentation that focuses on appeals to the audience’s values and involvement in design, which aid in creating a persuasive argument for the implementation of her technology.

Neri Oxman employs a harsh juxtaposition between manmade and natural language to appeal to the world’s rising belief that design should shift to more sustainable, environmentally friendly methods. In 2015, the same year as the TED Talk, the United Nations launched their sustainable development agenda to address the growing movement towards creating a more sustainable future. Part of the plan of action is the 2030 Agenda, which calls for creating “sustainable cities and communities” alongside “responsible production and consumption” (“Support Sustainable Development”). In the linguistic mode of the talk, Oxman “focuses on [her] language use and specific word choice” to appeal to the growing call for sustainable design (Carroll 55). Oxman highlights the difference between parts and continuous material, noting that a steel dome is “made of thousands of steel parts,” while the silk dome is made “of a single silk thread” (Oxman 00:01-00:26). The comparison between “thousands” and “single” emphasizes the difference in the large quantities of the world’s common assemblies to her new work. “Steel parts” and “silk thread” also illuminates that while her technology is still developed by man, it is entirely made of a component that has already existed in nature and was not manufactured by man. The manufacturing of “thousands” of man made steel parts can create environmentally harmful byproducts when being produced, while the cultivating the “silk thread” from a caterpillar creates no negative effect. Oxman continues that the steel dome is “synthetic,” and “the other [dome] organic” (Oxman 00:01-00:26). Directly pitting “synthetic” with “organic” suggests that Oxman’s work is produced in a more sustainable method than current design. “Organic” gives the impression that Oxman’s design can be decomposed in a safe manner, while “synthetic” brings connotations of materials that will either fail to decay, or will harm the environment. Through her careful language choice, Oxman creates an image of her work that emphasizes a new sustainable way of design in order to produce materials that do not generate negative environmental effects through their production or decomposition.

Oxman builds off of the juxtaposition to illuminate the similarities in overall function between her work and current design through the use of visual aids. The start of Oxman’s presentation immediately shows two different domes, side by side (See fig. 1), as she uses the visual aids to “position the spectator as an active participant in the making of meaning” (Benson 197). She places a manufactured dome on the left side of the visual, and her silk designed dome on the right side. As the viewer progresses from the left to right, they are invited to interpret the connection between the two domes. Oxman’s visual of her designed dome having a similar shape, structure, and background lighting pushes the audience to “make meaning” of the direct comparison between the two domes. The similarities in the visuals allows the audience to imply that the new design has an analogous function to the one of the currently accepted dome. Oxman continues by using a technique of reframing the photographs in order to “provide close-ups of [things] barely noticeable in the original photograph, thus inviting viewers to question why this is so” (Lancioni 398). She drastically zooms in on the two domes to show their structures (See fig. 2). The clear similarity in color and web-like appearance pushes the audience to draw the conclusion that the function and structure of the newly designed dome follows the current approach, but takes a new approach. Oxman then looks to influence the connotation the audience has with her new design by placing visuals of models wearing her work on modeling runways (See fig. 3). The visual of the technology in a modeling show encourages the audience to associate runway fashion qualities of luxury, high quality, and innovation with Oxman’s work. Oxman’s use of visual aids helps her subtly promote the message that her new technology has a proven function and is a higher quality product in comparison to current design.

After using visual aids to cement the logistical appeal of her design, Oxman seeks to appeal to the emotional response of the audience through the use of religious values within a biblical allusion. According to a Pew Research Center demographic analysis in 2015, Christians made up about 31% of the world’s population, the largest religious group in the world (Hackett and McClendon). Knowing that her talk will reach a large global audience due to TED’s prominence, Oxman “tailor[s] it to a particular audience” through the use of a biblical story (Herrick 19). Oxman recalls a biblical story where there was a fruit tree, but “there was to be no differentiation between trunk, branches, leaves and fruit. The whole tree was a fruit” (Oxman 5:40-6:42). She uses the story to emphasize that the tree is made of a single part, similar to the production of her own work. Immediately after introducing the story, Oxman claims that her team “looked for that biblical material,” and “found it” (Oxman 6:42-7:15). The description of finding biblical material creates an implicit comment on the material of Oxman’s design. Oxman attempts to “alter the symbolic framework [her audience employs] to organize their thinking” so that a religious audience immediately associates their predisposed, positive, biblical qualities with her work (Herrick 19). Oxman’s awareness of the audience and her talk’s outreach allows her to effectively persuade a portion of the audience by connecting her work to their deeper, unwavering personal values.

Oxman expands on her emotional appeal by establishing a personal bond between the audience and her work. She utilizes displaying human emotion and using collective pronouns in order to construct a personal bias for the audience’s view of her technology. Oxman introduces her team with a photo board of their headshots, and a picture of their hands next to them. Suddenly, the students begin to laugh and smile, while their hands begin working on different tasks next to each other (Oxman 2:29-3:27). The emotion that is displayed in the video allows Oxman to effectively “persuade through the character” of her team by showing the audience that they are not a distant group of scientists, but resemble the people sitting besides them (Selzer 287). The audience having a more grounded connection to the team enables Oxman to directly include the audience in her work, telling them that “[w]e live in a very special time in history, a rare time” (Oxman 2:29-3:27). The use of the collective “we” immediately places the audience within a context of Oxman’s work. They are not just observers of a creation, they are active participants in the change her work is bringing. Oxman also focuses on describing the time period as “special” and “rare” to give the audience a unique sense of relation to the project; they are the first ones throughout their genealogy who can experience Oxman’s work. Oxman’s ability to give the audience an interpersonal relation to her work puts them at a disposition to be more accepting of new views and potentially have a subtle bias towards the work they are connected to. Oxman’s progression from a logical comparison of her work and current design to an emotional appeal towards her audience’s values signals a focus on the connection between her technology and the audience.

A critical analysis of Oxman’s talk suggests that she uses a deliberative styled approach in her claims in order to achieve a single goal of having her work adopted by society. Selzer characterizes deliberative rhetoric as being “organized around the kinds of decisions a civic or social organization must make (about a future course of action)” (Selzer 284). Oxman describes her collection of wearable technology as “the future of our race on our planet and beyond,” which will help society “move away from the age of the machine” (Oxman 11:39-12:37). Oxman gives the audience a blueprint for the future of technology with her work, and proposes that the audience must choose to adopt the new technology over the current systems in place. The use of deliberative rhetoric establishes a forceful tone, suggesting that Oxman’s main goal is to push her technology for its own success.

While Oxman’s use of deliberative rhetoric may underline an ultimate goal to have her work adopted by the current world, her focus on incorporating epideictic rhetoric creates a more persuasive argument for adopting her work. By focusing on the community the audience is a part of, Oxman articulates an overall goal of improving society through reevaluating design beliefs, something that is more inclusive of the audience’s interests. Selzer claims that “in epideictic discourse, the audience is asked to reconsider beliefs and values” (Selzer 284). Oxman challenges the audience’s views on design by asking multiple questions, such as “[w]hat would design be like if objects were made of a single part?” (Oxman 5:40-6:42). Oxman specifically questions the current assemblies that are made up of thousands of parts, and are integrated into daily life. While her technology fits the criteria of being a single part, Oxman aims to question how the current world could view design in new lenses in order to improve. After asking the audience to reconsider the current state, she then asks, “[w]ould we return to a better state of creation?” (Oxman 5:40-6:42). Oxman asking if adopting new beliefs would not only change the current system of design, but “return” to a “better state” suggests that the present design process is flawed, but she does not immediately follow that her work is the solution. Oxman’s deliberative rhetoric tells the audience that they must accept her work, but the epideictic rhetoric gives the audience agency. The audience feels as though they are tasked in assessing how their community adopts design standards because Oxman makes them feel as though they have a choice in what the standards are. Owing to Oxman's strategic framing of her argument, the audience has the agency in deciding to adopt Oxman’s design and is able to enthusiastically drive the direction of the future.

Neri Oxman’s use of rhetorical strategies helps her create a persuasive argument for the audience to reconsider the design values that are currently being deployed. Oxman pushes her own technology, but also raises the question of how the world can adapt to more sustainable design through additive manufacturing. Can whole buildings be 3D printed with organic materials? Can a building be composed of a single component? By reconsidering how to design, the world can investigate new sustainable approaches to old and arising problems alike. With additive manufacturing, the future of human design may lead not only to new technological breakthroughs, but also a more symbiotic relationship between the Earth and its inhabitants.

a split screen of two different domes with light shining through each one

Works Cited

Benson, Thomas J. “Respecting the Reader.” Quarterly Journal of Speech , vol. 72, no. 2, 1986, pp. 197-204.

Carroll, Laura Bolin. “Backpacks vs. Briefcases: Steps Towards Rhetorical Analysis.” Writing Spaces: Readings on Writings, Vol. 1., edited by Charles Lowe and Pavel Zemliansky, Parlor Press, 2010, pp. 55.

Hackett, Conrad, and David McClendon. “Christians Remain World's Largest Religious Group, but They Are Declining in Europe.” Pew Research Center , Pew Research Center, 31 May 2020, https://www.pewresearch.org/fact-tank/2017/04/05/christians-remain-worlds-largest-religious-group-but-they-are-declining-in-europe/ .

Herrick, James A. “An Overview of Rhetoric.” The History of Theory of Rhetoric , 2nd ed., Allyn and Bacon, 2001, pp. 19.

Lancioni, Judith. “The rhetoric of the frame revisioning archival photographs in The Civil War .” Western Journal of Communication , vol. 60, no. 4, pp. 397-414.

Oxman, Neri. “Design at the Intersection of Technology and Biology.” TED , October, 2015, https://www.ted.com/talks/neri_oxman_design_at_the_intersection_of_technology_and_biology/transcript .

Selzer, Jack. “Rhetorical Analysis: Understanding How Texts Persuade Readers.” What Writing Does and How it Does It, edited by Charles Bazerman, Paul Prior, Routledge, 2003, pp. 284, 287.

“Support Sustainable Development and Climate Action.” United Nations . https://www.un.org/en/our-work/support-sustainable-development-and-climate-action . Accessed 6 March 2023.

how to write a discourse analysis essay

Jason Piechota

Jason Piechota is a sophomore at the University of Notre Dame studying Mechanical Engineering. Interested in design, Jason analyzed Neri Oxman’s TED Talk, “Design at the Intersection of Technology and Biology,” where Oxman outlines a new approach to design. His essay investigates the different rhetorical techniques employed by Oxman in order to promote biological additive manufacturing, which may prove to be a viable solution to many design problems. In the future, Jason hopes to find a job within the automotive industry, specifically focusing on design. He would like to thank his family and friends for their support, and Dr. McLaughlin for her help in forming his analysis.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 03 June 2024

Applying large language models for automated essay scoring for non-native Japanese

  • Wenchao Li 1 &
  • Haitao Liu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  723 ( 2024 ) Cite this article

129 Accesses

1 Altmetric

Metrics details

  • Language and linguistics

Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

Similar content being viewed by others

how to write a discourse analysis essay

Accurate structure prediction of biomolecular interactions with AlphaFold 3

how to write a discourse analysis essay

Testing theory of mind in large language models and humans

how to write a discourse analysis essay

Highly accurate protein structure prediction with AlphaFold

Conventional machine learning technology in aes.

AES has experienced significant growth with the advancement of machine learning technologies in recent decades. In the earlier stages of AES development, conventional machine learning-based approaches were commonly used. These approaches involved the following procedures: a) feeding the machine with a dataset. In this step, a dataset of essays is provided to the machine learning system. The dataset serves as the basis for training the model and establishing patterns and correlations between linguistic features and human ratings. b) the machine learning model is trained using linguistic features that best represent human ratings and can effectively discriminate learners’ writing proficiency. These features include lexical richness (Lu, 2012 ; Kyle and Crossley, 2015 ; Kyle et al. 2021 ), syntactic complexity (Lu, 2010 ; Liu, 2008 ), text cohesion (Crossley and McNamara, 2016 ), and among others. Conventional machine learning approaches in AES require human intervention, such as manual correction and annotation of essays. This human involvement was necessary to create a labeled dataset for training the model. Several AES systems have been developed using conventional machine learning technologies. These include the Intelligent Essay Assessor (Landauer et al. 2003 ), the e-rater engine by Educational Testing Service (Attali and Burstein, 2006 ; Burstein, 2003 ), MyAccess with the InterlliMetric scoring engine by Vantage Learning (Elliot, 2003 ), and the Bayesian Essay Test Scoring system (Rudner and Liang, 2002 ). These systems have played a significant role in automating the essay scoring process and providing quick and consistent feedback to learners. However, as touched upon earlier, conventional machine learning approaches rely on predetermined linguistic features and often require manual intervention, making them less flexible and potentially limiting their generalizability to different contexts.

In the context of the Japanese language, conventional machine learning-incorporated AES tools include Jess (Ishioka and Kameda, 2006 ) and JWriter (Lee and Hasebe, 2017 ). Jess assesses essays by deducting points from the perfect score, utilizing the Mainichi Daily News newspaper as a database. The evaluation criteria employed by Jess encompass various aspects, such as rhetorical elements (e.g., reading comprehension, vocabulary diversity, percentage of complex words, and percentage of passive sentences), organizational structures (e.g., forward and reverse connection structures), and content analysis (e.g., latent semantic indexing). JWriter employs linear regression analysis to assign weights to various measurement indices, such as average sentence length and total number of characters. These weights are then combined to derive the overall score. A pilot study involving the Jess model was conducted on 1320 essays at different proficiency levels, including primary, intermediate, and advanced. However, the results indicated that the Jess model failed to significantly distinguish between these essay levels. Out of the 16 measures used, four measures, namely median sentence length, median clause length, median number of phrases, and maximum number of phrases, did not show statistically significant differences between the levels. Additionally, two measures exhibited between-level differences but lacked linear progression: the number of attributives declined words and the Kanji/kana ratio. On the other hand, the remaining measures, including maximum sentence length, maximum clause length, number of attributive conjugated words, maximum number of consecutive infinitive forms, maximum number of conjunctive-particle clauses, k characteristic value, percentage of big words, and percentage of passive sentences, demonstrated statistically significant between-level differences and displayed linear progression.

Both Jess and JWriter exhibit notable limitations, including the manual selection of feature parameters and weights, which can introduce biases into the scoring process. The reliance on human annotators to label non-native language essays also introduces potential noise and variability in the scoring. Furthermore, an important concern is the possibility of system manipulation and cheating by learners who are aware of the regression equation utilized by the models (Hirao et al. 2020 ). These limitations emphasize the need for further advancements in AES systems to address these challenges.

Deep learning technology in AES

Deep learning has emerged as one of the approaches for improving the accuracy and effectiveness of AES. Deep learning-based AES methods utilize artificial neural networks that mimic the human brain’s functioning through layered algorithms and computational units. Unlike conventional machine learning, deep learning autonomously learns from the environment and past errors without human intervention. This enables deep learning models to establish nonlinear correlations, resulting in higher accuracy. Recent advancements in deep learning have led to the development of transformers, which are particularly effective in learning text representations. Noteworthy examples include bidirectional encoder representations from transformers (BERT) (Devlin et al. 2019 ) and the generative pretrained transformer (GPT) (OpenAI).

BERT is a linguistic representation model that utilizes a transformer architecture and is trained on two tasks: masked linguistic modeling and next-sentence prediction (Hirao et al. 2020 ; Vaswani et al. 2017 ). In the context of AES, BERT follows specific procedures, as illustrated in Fig. 1 : (a) the tokenized prompts and essays are taken as input; (b) special tokens, such as [CLS] and [SEP], are added to mark the beginning and separation of prompts and essays; (c) the transformer encoder processes the prompt and essay sequences, resulting in hidden layer sequences; (d) the hidden layers corresponding to the [CLS] tokens (T[CLS]) represent distributed representations of the prompts and essays; and (e) a multilayer perceptron uses these distributed representations as input to obtain the final score (Hirao et al. 2020 ).

figure 1

AES system with BERT (Hirao et al. 2020 ).

The training of BERT using a substantial amount of sentence data through the Masked Language Model (MLM) allows it to capture contextual information within the hidden layers. Consequently, BERT is expected to be capable of identifying artificial essays as invalid and assigning them lower scores (Mizumoto and Eguchi, 2023 ). In the context of AES for nonnative Japanese learners, Hirao et al. ( 2020 ) combined the long short-term memory (LSTM) model proposed by Hochreiter and Schmidhuber ( 1997 ) with BERT to develop a tailored automated Essay Scoring System. The findings of their study revealed that the BERT model outperformed both the conventional machine learning approach utilizing character-type features such as “kanji” and “hiragana”, as well as the standalone LSTM model. Takeuchi et al. ( 2021 ) presented an approach to Japanese AES that eliminates the requirement for pre-scored essays by relying solely on reference texts or a model answer for the essay task. They investigated multiple similarity evaluation methods, including frequency of morphemes, idf values calculated on Wikipedia, LSI, LDA, word-embedding vectors, and document vectors produced by BERT. The experimental findings revealed that the method utilizing the frequency of morphemes with idf values exhibited the strongest correlation with human-annotated scores across different essay tasks. The utilization of BERT in AES encounters several limitations. Firstly, essays often exceed the model’s maximum length limit. Second, only score labels are available for training, which restricts access to additional information.

Mizumoto and Eguchi ( 2023 ) were pioneers in employing the GPT model for AES in non-native English writing. Their study focused on evaluating the accuracy and reliability of AES using the GPT-3 text-davinci-003 model, analyzing a dataset of 12,100 essays from the corpus of nonnative written English (TOEFL11). The findings indicated that AES utilizing the GPT-3 model exhibited a certain degree of accuracy and reliability. They suggest that GPT-3-based AES systems hold the potential to provide support for human ratings. However, applying GPT model to AES presents a unique natural language processing (NLP) task that involves considerations such as nonnative language proficiency, the influence of the learner’s first language on the output in the target language, and identifying linguistic features that best indicate writing quality in a specific language. These linguistic features may differ morphologically or syntactically from those present in the learners’ first language, as observed in (1)–(3).

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

1 sg .-give. past- him-one .cl- book

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

Kare-ni-hon-o-age-mashi-ta

3 sg .- dat -hon- acc- give.honorification. past

Inflectional

give, give-s, gave, given, giving

Additionally, the morphological agglutination and subject-object-verb (SOV) order in Japanese, along with its idiomatic expressions, pose additional challenges for applying language models in AES tasks (4).

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

“My leg became like a stick (I am extremely tired).”

The example sentence provided demonstrates the morpho-syntactic structure of Japanese and the presence of an idiomatic expression. In this sentence, the verb “なる” (naru), meaning “to become”, appears at the end of the sentence. The verb stem “なり” (nari) is attached with morphemes indicating honorification (“ます” - mashu) and tense (“た” - ta), showcasing agglutination. While the sentence can be literally translated as “my leg became like a stick”, it carries an idiomatic interpretation that implies “I am extremely tired”.

To overcome this issue, CyberAgent Inc. ( 2023 ) has developed the Open-Calm series of language models specifically designed for Japanese. Open-Calm consists of pre-trained models available in various sizes, such as Small, Medium, Large, and 7b. Figure 2 depicts the fundamental structure of the Open-Calm model. A key feature of this architecture is the incorporation of the Lora Adapter and GPT-NeoX frameworks, which can enhance its language processing capabilities.

figure 2

GPT-NeoX Model Architecture (Okgetheng and Takeuchi 2024 ).

In a recent study conducted by Okgetheng and Takeuchi ( 2024 ), they assessed the efficacy of Open-Calm language models in grading Japanese essays. The research utilized a dataset of approximately 300 essays, which were annotated by native Japanese educators. The findings of the study demonstrate the considerable potential of Open-Calm language models in automated Japanese essay scoring. Specifically, among the Open-Calm family, the Open-Calm Large model (referred to as OCLL) exhibited the highest performance. However, it is important to note that, as of the current date, the Open-Calm Large model does not offer public access to its server. Consequently, users are required to independently deploy and operate the environment for OCLL. In order to utilize OCLL, users must have a PC equipped with an NVIDIA GeForce RTX 3060 (8 or 12 GB VRAM).

In summary, while the potential of LLMs in automated scoring of nonnative Japanese essays has been demonstrated in two studies—BERT-driven AES (Hirao et al. 2020 ) and OCLL-based AES (Okgetheng and Takeuchi, 2024 )—the number of research efforts in this area remains limited.

Another significant challenge in applying LLMs to AES lies in prompt engineering and ensuring its reliability and effectiveness (Brown et al. 2020 ; Rae et al. 2021 ; Zhang et al. 2021 ). Various prompting strategies have been proposed, such as the zero-shot chain of thought (CoT) approach (Kojima et al. 2022 ), which involves manually crafting diverse and effective examples. However, manual efforts can lead to mistakes. To address this, Zhang et al. ( 2021 ) introduced an automatic CoT prompting method called Auto-CoT, which demonstrates matching or superior performance compared to the CoT paradigm. Another prompt framework is trees of thoughts, enabling a model to self-evaluate its progress at intermediate stages of problem-solving through deliberate reasoning (Yao et al. 2023 ).

Beyond linguistic studies, there has been a noticeable increase in the number of foreign workers in Japan and Japanese learners worldwide (Ministry of Health, Labor, and Welfare of Japan, 2022 ; Japan Foundation, 2021 ). However, existing assessment methods, such as the Japanese Language Proficiency Test (JLPT), J-CAT, and TTBJ Footnote 1 , primarily focus on reading, listening, vocabulary, and grammar skills, neglecting the evaluation of writing proficiency. As the number of workers and language learners continues to grow, there is a rising demand for an efficient AES system that can reduce costs and time for raters and be utilized for employment, examinations, and self-study purposes.

This study aims to explore the potential of LLM-based AES by comparing the effectiveness of five models: two LLMs (GPT Footnote 2 and BERT), one Japanese local LLM (OCLL), and two conventional machine learning-based methods (linguistic feature-based scoring tools - Jess and JWriter).

The research questions addressed in this study are as follows:

To what extent do the LLM-driven AES and linguistic feature-based AES, when used as automated tools to support human rating, accurately reflect test takers’ actual performance?

What influence does the prompt have on the accuracy and performance of LLM-based AES methods?

The subsequent sections of the manuscript cover the methodology, including the assessment measures for nonnative Japanese writing proficiency, criteria for prompts, and the dataset. The evaluation section focuses on the analysis of annotations and rating scores generated by LLM-driven and linguistic feature-based AES methods.

Methodology

The dataset utilized in this study was obtained from the International Corpus of Japanese as a Second Language (I-JAS) Footnote 3 . This corpus consisted of 1000 participants who represented 12 different first languages. For the study, the participants were given a story-writing task on a personal computer. They were required to write two stories based on the 4-panel illustrations titled “Picnic” and “The key” (see Appendix A). Background information for the participants was provided by the corpus, including their Japanese language proficiency levels assessed through two online tests: J-CAT and SPOT. These tests evaluated their reading, listening, vocabulary, and grammar abilities. The learners’ proficiency levels were categorized into six levels aligned with the Common European Framework of Reference for Languages (CEFR) and the Reference Framework for Japanese Language Education (RFJLE): A1, A2, B1, B2, C1, and C2. According to Lee et al. ( 2015 ), there is a high level of agreement (r = 0.86) between the J-CAT and SPOT assessments, indicating that the proficiency certifications provided by J-CAT are consistent with those of SPOT. However, it is important to note that the scores of J-CAT and SPOT do not have a one-to-one correspondence. In this study, the J-CAT scores were used as a benchmark to differentiate learners of different proficiency levels. A total of 1400 essays were utilized, representing the beginner (aligned with A1), A2, B1, B2, C1, and C2 levels based on the J-CAT scores. Table 1 provides information about the learners’ proficiency levels and their corresponding J-CAT and SPOT scores.

A dataset comprising a total of 1400 essays from the story writing tasks was collected. Among these, 714 essays were utilized to evaluate the reliability of the LLM-based AES method, while the remaining 686 essays were designated as development data to assess the LLM-based AES’s capability to distinguish participants with varying proficiency levels. The GPT 4 API was used in this study. A detailed explanation of the prompt-assessment criteria is provided in Section Prompt . All essays were sent to the model for measurement and scoring.

Measures of writing proficiency for nonnative Japanese

Japanese exhibits a morphologically agglutinative structure where morphemes are attached to the word stem to convey grammatical functions such as tense, aspect, voice, and honorifics, e.g. (5).

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

[eat (stem)-causative-passive voice-honorification-tense. past-question marker]

Japanese employs nine case particles to indicate grammatical functions: the nominative case particle が (ga), the accusative case particle を (o), the genitive case particle の (no), the dative case particle に (ni), the locative/instrumental case particle で (de), the ablative case particle から (kara), the directional case particle へ (e), and the comitative case particle と (to). The agglutinative nature of the language, combined with the case particle system, provides an efficient means of distinguishing between active and passive voice, either through morphemes or case particles, e.g. 食べる taberu “eat concusive . ” (active voice); 食べられる taberareru “eat concusive . ” (passive voice). In the active voice, “パン を 食べる” (pan o taberu) translates to “to eat bread”. On the other hand, in the passive voice, it becomes “パン が 食べられた” (pan ga taberareta), which means “(the) bread was eaten”. Additionally, it is important to note that different conjugations of the same lemma are considered as one type in order to ensure a comprehensive assessment of the language features. For example, e.g., 食べる taberu “eat concusive . ”; 食べている tabeteiru “eat progress .”; 食べた tabeta “eat past . ” as one type.

To incorporate these features, previous research (Suzuki, 1999 ; Watanabe et al. 1988 ; Ishioka, 2001 ; Ishioka and Kameda, 2006 ; Hirao et al. 2020 ) has identified complexity, fluency, and accuracy as crucial factors for evaluating writing quality. These criteria are assessed through various aspects, including lexical richness (lexical density, diversity, and sophistication), syntactic complexity, and cohesion (Kyle et al. 2021 ; Mizumoto and Eguchi, 2023 ; Ure, 1971 ; Halliday, 1985 ; Barkaoui and Hadidi, 2020 ; Zenker and Kyle, 2021 ; Kim et al. 2018 ; Lu, 2017 ; Ortega, 2015 ). Therefore, this study proposes five scoring categories: lexical richness, syntactic complexity, cohesion, content elaboration, and grammatical accuracy. A total of 16 measures were employed to capture these categories. The calculation process and specific details of these measures can be found in Table 2 .

T-unit, first introduced by Hunt ( 1966 ), is a measure used for evaluating speech and composition. It serves as an indicator of syntactic development and represents the shortest units into which a piece of discourse can be divided without leaving any sentence fragments. In the context of Japanese language assessment, Sakoda and Hosoi ( 2020 ) utilized T-unit as the basic unit to assess the accuracy and complexity of Japanese learners’ speaking and storytelling. The calculation of T-units in Japanese follows the following principles:

A single main clause constitutes 1 T-unit, regardless of the presence or absence of dependent clauses, e.g. (6).

ケンとマリはピクニックに行きました (main clause): 1 T-unit.

If a sentence contains a main clause along with subclauses, each subclause is considered part of the same T-unit, e.g. (7).

天気が良かった の で (subclause)、ケンとマリはピクニックに行きました (main clause): 1 T-unit.

In the case of coordinate clauses, where multiple clauses are connected, each coordinated clause is counted separately. Thus, a sentence with coordinate clauses may have 2 T-units or more, e.g. (8).

ケンは地図で場所を探して (coordinate clause)、マリはサンドイッチを作りました (coordinate clause): 2 T-units.

Lexical diversity refers to the range of words used within a text (Engber, 1995 ; Kyle et al. 2021 ) and is considered a useful measure of the breadth of vocabulary in L n production (Jarvis, 2013a , 2013b ).

The type/token ratio (TTR) is widely recognized as a straightforward measure for calculating lexical diversity and has been employed in numerous studies. These studies have demonstrated a strong correlation between TTR and other methods of measuring lexical diversity (e.g., Bentz et al. 2016 ; Čech and Miroslav, 2018 ; Çöltekin and Taraka, 2018 ). TTR is computed by considering both the number of unique words (types) and the total number of words (tokens) in a given text. Given that the length of learners’ writing texts can vary, this study employs the moving average type-token ratio (MATTR) to mitigate the influence of text length. MATTR is calculated using a 50-word moving window. Initially, a TTR is determined for words 1–50 in an essay, followed by words 2–51, 3–52, and so on until the end of the essay is reached (Díez-Ortega and Kyle, 2023 ). The final MATTR scores were obtained by averaging the TTR scores for all 50-word windows. The following formula was employed to derive MATTR:

\({\rm{MATTR}}({\rm{W}})=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{N}}-{\rm{W}}+1}{{\rm{F}}}_{{\rm{i}}}}{{\rm{W}}({\rm{N}}-{\rm{W}}+1)}\)

Here, N refers to the number of tokens in the corpus. W is the randomly selected token size (W < N). \({F}_{i}\) is the number of types in each window. The \({\rm{MATTR}}({\rm{W}})\) is the mean of a series of type-token ratios (TTRs) based on the word form for all windows. It is expected that individuals with higher language proficiency will produce texts with greater lexical diversity, as indicated by higher MATTR scores.

Lexical density was captured by the ratio of the number of lexical words to the total number of words (Lu, 2012 ). Lexical sophistication refers to the utilization of advanced vocabulary, often evaluated through word frequency indices (Crossley et al. 2013 ; Haberman, 2008 ; Kyle and Crossley, 2015 ; Laufer and Nation, 1995 ; Lu, 2012 ; Read, 2000 ). In line of writing, lexical sophistication can be interpreted as vocabulary breadth, which entails the appropriate usage of vocabulary items across various lexicon-grammatical contexts and registers (Garner et al. 2019 ; Kim et al. 2018 ; Kyle et al. 2018 ). In Japanese specifically, words are considered lexically sophisticated if they are not included in the “Japanese Education Vocabulary List Ver 1.0”. Footnote 4 Consequently, lexical sophistication was calculated by determining the number of sophisticated word types relative to the total number of words per essay. Furthermore, it has been suggested that, in Japanese writing, sentences should ideally have a length of no more than 40 to 50 characters, as this promotes readability. Therefore, the median and maximum sentence length can be considered as useful indices for assessment (Ishioka and Kameda, 2006 ).

Syntactic complexity was assessed based on several measures, including the mean length of clauses, verb phrases per T-unit, clauses per T-unit, dependent clauses per T-unit, complex nominals per clause, adverbial clauses per clause, coordinate phrases per clause, and mean dependency distance (MDD). The MDD reflects the distance between the governor and dependent positions in a sentence. A larger dependency distance indicates a higher cognitive load and greater complexity in syntactic processing (Liu, 2008 ; Liu et al. 2017 ). The MDD has been established as an efficient metric for measuring syntactic complexity (Jiang, Quyang, and Liu, 2019 ; Li and Yan, 2021 ). To calculate the MDD, the position numbers of the governor and dependent are subtracted, assuming that words in a sentence are assigned in a linear order, such as W1 … Wi … Wn. In any dependency relationship between words Wa and Wb, Wa is the governor and Wb is the dependent. The MDD of the entire sentence was obtained by taking the absolute value of governor – dependent:

MDD = \(\frac{1}{n}{\sum }_{i=1}^{n}|{\rm{D}}{{\rm{D}}}_{i}|\)

In this formula, \(n\) represents the number of words in the sentence, and \({DD}i\) is the dependency distance of the \({i}^{{th}}\) dependency relationship of a sentence. Building on this, the annotation of sentence ‘Mary-ga-John-ni-keshigomu-o-watashita was [Mary- top -John- dat -eraser- acc -give- past] ’. The sentence’s MDD would be 2. Table 3 provides the CSV file as a prompt for GPT 4.

Cohesion (semantic similarity) and content elaboration aim to capture the ideas presented in test taker’s essays. Cohesion was assessed using three measures: Synonym overlap/paragraph (topic), Synonym overlap/paragraph (keywords), and word2vec cosine similarity. Content elaboration and development were measured as the number of metadiscourse markers (type)/number of words. To capture content closely, this study proposed a novel-distance based representation, by encoding the cosine distance between the essay (by learner) and essay task’s (topic and keyword) i -vectors. The learner’s essay is decoded into a word sequence, and aligned to the essay task’ topic and keyword for log-likelihood measurement. The cosine distance reveals the content elaboration score in the leaners’ essay. The mathematical equation of cosine similarity between target-reference vectors is shown in (11), assuming there are i essays and ( L i , …. L n ) and ( N i , …. N n ) are the vectors representing the learner and task’s topic and keyword respectively. The content elaboration distance between L i and N i was calculated as follows:

\(\cos \left(\theta \right)=\frac{{\rm{L}}\,\cdot\, {\rm{N}}}{\left|{\rm{L}}\right|{\rm{|N|}}}=\frac{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}{N}_{i}}{\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}^{2}}\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{N}_{i}^{2}}}\)

A high similarity value indicates a low difference between the two recognition outcomes, which in turn suggests a high level of proficiency in content elaboration.

To evaluate the effectiveness of the proposed measures in distinguishing different proficiency levels among nonnative Japanese speakers’ writing, we conducted a multi-faceted Rasch measurement analysis (Linacre, 1994 ). This approach applies measurement models to thoroughly analyze various factors that can influence test outcomes, including test takers’ proficiency, item difficulty, and rater severity, among others. The underlying principles and functionality of multi-faceted Rasch measurement are illustrated in (12).

\(\log \left(\frac{{P}_{{nijk}}}{{P}_{{nij}(k-1)}}\right)={B}_{n}-{D}_{i}-{C}_{j}-{F}_{k}\)

(12) defines the logarithmic transformation of the probability ratio ( P nijk /P nij(k-1) )) as a function of multiple parameters. Here, n represents the test taker, i denotes a writing proficiency measure, j corresponds to the human rater, and k represents the proficiency score. The parameter B n signifies the proficiency level of test taker n (where n ranges from 1 to N). D j represents the difficulty parameter of test item i (where i ranges from 1 to L), while C j represents the severity of rater j (where j ranges from 1 to J). Additionally, F k represents the step difficulty for a test taker to move from score ‘k-1’ to k . P nijk refers to the probability of rater j assigning score k to test taker n for test item i . P nij(k-1) represents the likelihood of test taker n being assigned score ‘k-1’ by rater j for test item i . Each facet within the test is treated as an independent parameter and estimated within the same reference framework. To evaluate the consistency of scores obtained through both human and computer analysis, we utilized the Infit mean-square statistic. This statistic is a chi-square measure divided by the degrees of freedom and is weighted with information. It demonstrates higher sensitivity to unexpected patterns in responses to items near a person’s proficiency level (Linacre, 2002 ). Fit statistics are assessed based on predefined thresholds for acceptable fit. For the Infit MNSQ, which has a mean of 1.00, different thresholds have been suggested. Some propose stricter thresholds ranging from 0.7 to 1.3 (Bond et al. 2021 ), while others suggest more lenient thresholds ranging from 0.5 to 1.5 (Eckes, 2009 ). In this study, we adopted the criterion of 0.70–1.30 for the Infit MNSQ.

Moving forward, we can now proceed to assess the effectiveness of the 16 proposed measures based on five criteria for accurately distinguishing various levels of writing proficiency among non-native Japanese speakers. To conduct this evaluation, we utilized the development dataset from the I-JAS corpus, as described in Section Dataset . Table 4 provides a measurement report that presents the performance details of the 14 metrics under consideration. The measure separation was found to be 4.02, indicating a clear differentiation among the measures. The reliability index for the measure separation was 0.891, suggesting consistency in the measurement. Similarly, the person separation reliability index was 0.802, indicating the accuracy of the assessment in distinguishing between individuals. All 16 measures demonstrated Infit mean squares within a reasonable range, ranging from 0.76 to 1.28. The Synonym overlap/paragraph (topic) measure exhibited a relatively high outfit mean square of 1.46, although the Infit mean square falls within an acceptable range. The standard error for the measures ranged from 0.13 to 0.28, indicating the precision of the estimates.

Table 5 further illustrated the weights assigned to different linguistic measures for score prediction, with higher weights indicating stronger correlations between those measures and higher scores. Specifically, the following measures exhibited higher weights compared to others: moving average type token ratio per essay has a weight of 0.0391. Mean dependency distance had a weight of 0.0388. Mean length of clause, calculated by dividing the number of words by the number of clauses, had a weight of 0.0374. Complex nominals per T-unit, calculated by dividing the number of complex nominals by the number of T-units, had a weight of 0.0379. Coordinate phrases rate, calculated by dividing the number of coordinate phrases by the number of clauses, had a weight of 0.0325. Grammatical error rate, representing the number of errors per essay, had a weight of 0.0322.

Criteria (output indicator)

The criteria used to evaluate the writing ability in this study were based on CEFR, which follows a six-point scale ranging from A1 to C2. To assess the quality of Japanese writing, the scoring criteria from Table 6 were utilized. These criteria were derived from the IELTS writing standards and served as assessment guidelines and prompts for the written output.

A prompt is a question or detailed instruction that is provided to the model to obtain a proper response. After several pilot experiments, we decided to provide the measures (Section Measures of writing proficiency for nonnative Japanese ) as the input prompt and use the criteria (Section Criteria (output indicator) ) as the output indicator. Regarding the prompt language, considering that the LLM was tasked with rating Japanese essays, would prompt in Japanese works better Footnote 5 ? We conducted experiments comparing the performance of GPT-4 using both English and Japanese prompts. Additionally, we utilized the Japanese local model OCLL with Japanese prompts. Multiple trials were conducted using the same sample. Regardless of the prompt language used, we consistently obtained the same grading results with GPT-4, which assigned a grade of B1 to the writing sample. This suggested that GPT-4 is reliable and capable of producing consistent ratings regardless of the prompt language. On the other hand, when we used Japanese prompts with the Japanese local model “OCLL”, we encountered inconsistent grading results. Out of 10 attempts with OCLL, only 6 yielded consistent grading results (B1), while the remaining 4 showed different outcomes, including A1 and B2 grades. These findings indicated that the language of the prompt was not the determining factor for reliable AES. Instead, the size of the training data and the model parameters played crucial roles in achieving consistent and reliable AES results for the language model.

The following is the utilized prompt, which details all measures and requires the LLM to score the essays using holistic and trait scores.

Please evaluate Japanese essays written by Japanese learners and assign a score to each essay on a six-point scale, ranging from A1, A2, B1, B2, C1 to C2. Additionally, please provide trait scores and display the calculation process for each trait score. The scoring should be based on the following criteria:

Moving average type-token ratio.

Number of lexical words (token) divided by the total number of words per essay.

Number of sophisticated word types divided by the total number of words per essay.

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

Number of metadiscourse markers (types) divided by the total number of words.

Number of errors per essay.

Japanese essay text

出かける前に二人が地図を見ている間に、サンドイッチを入れたバスケットに犬が入ってしまいました。それに気づかずに二人は楽しそうに出かけて行きました。やがて突然犬がバスケットから飛び出し、二人は驚きました。バスケット の 中を見ると、食べ物はすべて犬に食べられていて、二人は困ってしまいました。(ID_JJJ01_SW1)

The score of the example above was B1. Figure 3 provides an example of holistic and trait scores provided by GPT-4 (with a prompt indicating all measures) via Bing Footnote 6 .

figure 3

Example of GPT-4 AES and feedback (with a prompt indicating all measures).

Statistical analysis

The aim of this study is to investigate the potential use of LLM for nonnative Japanese AES. It seeks to compare the scoring outcomes obtained from feature-based AES tools, which rely on conventional machine learning technology (i.e. Jess, JWriter), with those generated by AI-driven AES tools utilizing deep learning technology (BERT, GPT, OCLL). To assess the reliability of a computer-assisted annotation tool, the study initially established human-human agreement as the benchmark measure. Subsequently, the performance of the LLM-based method was evaluated by comparing it to human-human agreement.

To assess annotation agreement, the study employed standard measures such as precision, recall, and F-score (Brants 2000 ; Lu 2010 ), along with the quadratically weighted kappa (QWK) to evaluate the consistency and agreement in the annotation process. Assume A and B represent human annotators. When comparing the annotations of the two annotators, the following results are obtained. The evaluation of precision, recall, and F-score metrics was illustrated in equations (13) to (15).

\({\rm{Recall}}(A,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,A}\)

\({\rm{Precision}}(A,\,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,B}\)

The F-score is the harmonic mean of recall and precision:

\({\rm{F}}-{\rm{score}}=\frac{2* ({\rm{Precision}}* {\rm{Recall}})}{{\rm{Precision}}+{\rm{Recall}}}\)

The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.

In accordance with Taghipour and Ng ( 2016 ), the calculation of QWK involves two steps:

Step 1: Construct a weight matrix W as follows:

\({W}_{{ij}}=\frac{{(i-j)}^{2}}{{(N-1)}^{2}}\)

i represents the annotation made by the tool, while j represents the annotation made by a human rater. N denotes the total number of possible annotations. Matrix O is subsequently computed, where O_( i, j ) represents the count of data annotated by the tool ( i ) and the human annotator ( j ). On the other hand, E refers to the expected count matrix, which undergoes normalization to ensure that the sum of elements in E matches the sum of elements in O.

Step 2: With matrices O and E, the QWK is obtained as follows:

K = 1- \(\frac{\sum i,j{W}_{i,j}\,{O}_{i,j}}{\sum i,j{W}_{i,j}\,{E}_{i,j}}\)

The value of the quadratic weighted kappa increases as the level of agreement improves. Further, to assess the accuracy of LLM scoring, the proportional reductive mean square error (PRMSE) was employed. The PRMSE approach takes into account the variability observed in human ratings to estimate the rater error, which is then subtracted from the variance of the human labels. This calculation provides an overall measure of agreement between the automated scores and true scores (Haberman et al. 2015 ; Loukina et al. 2020 ; Taghipour and Ng, 2016 ). The computation of PRMSE involves the following steps:

Step 1: Calculate the mean squared errors (MSEs) for the scoring outcomes of the computer-assisted tool (MSE tool) and the human scoring outcomes (MSE human).

Step 2: Determine the PRMSE by comparing the MSE of the computer-assisted tool (MSE tool) with the MSE from human raters (MSE human), using the following formula:

\({\rm{PRMSE}}=1-\frac{({\rm{MSE}}\,{\rm{tool}})\,}{({\rm{MSE}}\,{\rm{human}})\,}=1-\,\frac{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-{\hat{{\rm{y}}}}_{{\rm{i}}})}^{2}}{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-\hat{{\rm{y}}})}^{2}}\)

In the numerator, ŷi represents the scoring outcome predicted by a specific LLM-driven AES system for a given sample. The term y i − ŷ i represents the difference between this predicted outcome and the mean value of all LLM-driven AES systems’ scoring outcomes. It quantifies the deviation of the specific LLM-driven AES system’s prediction from the average prediction of all LLM-driven AES systems. In the denominator, y i − ŷ represents the difference between the scoring outcome provided by a specific human rater for a given sample and the mean value of all human raters’ scoring outcomes. It measures the discrepancy between the specific human rater’s score and the average score given by all human raters. The PRMSE is then calculated by subtracting the ratio of the MSE tool to the MSE human from 1. PRMSE falls within the range of 0 to 1, with larger values indicating reduced errors in LLM’s scoring compared to those of human raters. In other words, a higher PRMSE implies that LLM’s scoring demonstrates greater accuracy in predicting the true scores (Loukina et al. 2020 ). The interpretation of kappa values, ranging from 0 to 1, is based on the work of Landis and Koch ( 1977 ). Specifically, the following categories are assigned to different ranges of kappa values: −1 indicates complete inconsistency, 0 indicates random agreement, 0.0 ~ 0.20 indicates extremely low level of agreement (slight), 0.21 ~ 0.40 indicates moderate level of agreement (fair), 0.41 ~ 0.60 indicates medium level of agreement (moderate), 0.61 ~ 0.80 indicates high level of agreement (substantial), 0.81 ~ 1 indicates almost perfect level of agreement. All statistical analyses were executed using Python script.

Results and discussion

Annotation reliability of the llm.

This section focuses on assessing the reliability of the LLM’s annotation and scoring capabilities. To evaluate the reliability, several tests were conducted simultaneously, aiming to achieve the following objectives:

Assess the LLM’s ability to differentiate between test takers with varying levels of oral proficiency.

Determine the level of agreement between the annotations and scoring performed by the LLM and those done by human raters.

The evaluation of the results encompassed several metrics, including: precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement.

Inter-annotator agreement (human–human annotator agreement)

We started with an agreement test of the two human annotators. Two trained annotators were recruited to determine the writing task data measures. A total of 714 scripts, as the test data, was utilized. Each analysis lasted 300–360 min. Inter-annotator agreement was evaluated using the standard measures of precision, recall, and F-score and QWK. Table 7 presents the inter-annotator agreement for the various indicators. As shown, the inter-annotator agreement was fairly high, with F-scores ranging from 1.0 for sentence and word number to 0.666 for grammatical errors.

The findings from the QWK analysis provided further confirmation of the inter-annotator agreement. The QWK values covered a range from 0.950 ( p  = 0.000) for sentence and word number to 0.695 for synonym overlap number (keyword) and grammatical errors ( p  = 0.001).

Agreement of annotation outcomes between human and LLM

To evaluate the consistency between human annotators and LLM annotators (BERT, GPT, OCLL) across the indices, the same test was conducted. The results of the inter-annotator agreement (F-score) between LLM and human annotation are provided in Appendix B-D. The F-scores ranged from 0.706 for Grammatical error # for OCLL-human to a perfect 1.000 for GPT-human, for sentences, clauses, T-units, and words. These findings were further supported by the QWK analysis, which showed agreement levels ranging from 0.807 ( p  = 0.001) for metadiscourse markers for OCLL-human to 0.962 for words ( p  = 0.000) for GPT-human. The findings demonstrated that the LLM annotation achieved a significant level of accuracy in identifying measurement units and counts.

Reliability of LLM-driven AES’s scoring and discriminating proficiency levels

This section examines the reliability of the LLM-driven AES scoring through a comparison of the scoring outcomes produced by human raters and the LLM ( Reliability of LLM-driven AES scoring ). It also assesses the effectiveness of the LLM-based AES system in differentiating participants with varying proficiency levels ( Reliability of LLM-driven AES discriminating proficiency levels ).

Reliability of LLM-driven AES scoring

Table 8 summarizes the QWK coefficient analysis between the scores computed by the human raters and the GPT-4 for the individual essays from I-JAS Footnote 7 . As shown, the QWK of all measures ranged from k  = 0.819 for lexical density (number of lexical words (tokens)/number of words per essay) to k  = 0.644 for word2vec cosine similarity. Table 9 further presents the Pearson correlations between the 16 writing proficiency measures scored by human raters and GPT 4 for the individual essays. The correlations ranged from 0.672 for syntactic complexity to 0.734 for grammatical accuracy. The correlations between the writing proficiency scores assigned by human raters and the BERT-based AES system were found to range from 0.661 for syntactic complexity to 0.713 for grammatical accuracy. The correlations between the writing proficiency scores given by human raters and the OCLL-based AES system ranged from 0.654 for cohesion to 0.721 for grammatical accuracy. These findings indicated an alignment between the assessments made by human raters and both the BERT-based and OCLL-based AES systems in terms of various aspects of writing proficiency.

Reliability of LLM-driven AES discriminating proficiency levels

After validating the reliability of the LLM’s annotation and scoring, the subsequent objective was to evaluate its ability to distinguish between various proficiency levels. For this analysis, a dataset of 686 individual essays was utilized. Table 10 presents a sample of the results, summarizing the means, standard deviations, and the outcomes of the one-way ANOVAs based on the measures assessed by the GPT-4 model. A post hoc multiple comparison test, specifically the Bonferroni test, was conducted to identify any potential differences between pairs of levels.

As the results reveal, seven measures presented linear upward or downward progress across the three proficiency levels. These were marked in bold in Table 10 and comprise one measure of lexical richness, i.e. MATTR (lexical diversity); four measures of syntactic complexity, i.e. MDD (mean dependency distance), MLC (mean length of clause), CNT (complex nominals per T-unit), CPC (coordinate phrases rate); one cohesion measure, i.e. word2vec cosine similarity and GER (grammatical error rate). Regarding the ability of the sixteen measures to distinguish adjacent proficiency levels, the Bonferroni tests indicated that statistically significant differences exist between the primary level and the intermediate level for MLC and GER. One measure of lexical richness, namely LD, along with three measures of syntactic complexity (VPT, CT, DCT, ACC), two measures of cohesion (SOPT, SOPK), and one measure of content elaboration (IMM), exhibited statistically significant differences between proficiency levels. However, these differences did not demonstrate a linear progression between adjacent proficiency levels. No significant difference was observed in lexical sophistication between proficiency levels.

To summarize, our study aimed to evaluate the reliability and differentiation capabilities of the LLM-driven AES method. For the first objective, we assessed the LLM’s ability to differentiate between test takers with varying levels of oral proficiency using precision, recall, F-Score, and quadratically-weighted kappa. Regarding the second objective, we compared the scoring outcomes generated by human raters and the LLM to determine the level of agreement. We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency measures for the individual essays. The results confirmed the feasibility of using the LLM for annotation and scoring in AES for nonnative Japanese. As a result, Research Question 1 has been addressed.

Comparison of BERT-, GPT-, OCLL-based AES, and linguistic-feature-based computation methods

This section aims to compare the effectiveness of five AES methods for nonnative Japanese writing, i.e. LLM-driven approaches utilizing BERT, GPT, and OCLL, linguistic feature-based approaches using Jess and JWriter. The comparison was conducted by comparing the ratings obtained from each approach with human ratings. All ratings were derived from the dataset introduced in Dataset . To facilitate the comparison, the agreement between the automated methods and human ratings was assessed using QWK and PRMSE. The performance of each approach was summarized in Table 11 .

The QWK coefficient values indicate that LLMs (GPT, BERT, OCLL) and human rating outcomes demonstrated higher agreement compared to feature-based AES methods (Jess and JWriter) in assessing writing proficiency criteria, including lexical richness, syntactic complexity, content, and grammatical accuracy. Among the LLMs, the GPT-4 driven AES and human rating outcomes showed the highest agreement in all criteria, except for syntactic complexity. The PRMSE values suggest that the GPT-based method outperformed linguistic feature-based methods and other LLM-based approaches. Moreover, an interesting finding emerged during the study: the agreement coefficient between GPT-4 and human scoring was even higher than the agreement between different human raters themselves. This discovery highlights the advantage of GPT-based AES over human rating. Ratings involve a series of processes, including reading the learners’ writing, evaluating the content and language, and assigning scores. Within this chain of processes, various biases can be introduced, stemming from factors such as rater biases, test design, and rating scales. These biases can impact the consistency and objectivity of human ratings. GPT-based AES may benefit from its ability to apply consistent and objective evaluation criteria. By prompting the GPT model with detailed writing scoring rubrics and linguistic features, potential biases in human ratings can be mitigated. The model follows a predefined set of guidelines and does not possess the same subjective biases that human raters may exhibit. This standardization in the evaluation process contributes to the higher agreement observed between GPT-4 and human scoring. Section Prompt strategy of the study delves further into the role of prompts in the application of LLMs to AES. It explores how the choice and implementation of prompts can impact the performance and reliability of LLM-based AES methods. Furthermore, it is important to acknowledge the strengths of the local model, i.e. the Japanese local model OCLL, which excels in processing certain idiomatic expressions. Nevertheless, our analysis indicated that GPT-4 surpasses local models in AES. This superior performance can be attributed to the larger parameter size of GPT-4, estimated to be between 500 billion and 1 trillion, which exceeds the sizes of both BERT and the local model OCLL.

Prompt strategy

In the context of prompt strategy, Mizumoto and Eguchi ( 2023 ) conducted a study where they applied the GPT-3 model to automatically score English essays in the TOEFL test. They found that the accuracy of the GPT model alone was moderate to fair. However, when they incorporated linguistic measures such as cohesion, syntactic complexity, and lexical features alongside the GPT model, the accuracy significantly improved. This highlights the importance of prompt engineering and providing the model with specific instructions to enhance its performance. In this study, a similar approach was taken to optimize the performance of LLMs. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. Model 1 was used as the baseline, representing GPT-4 without any additional prompting. Model 2, on the other hand, involved GPT-4 prompted with 16 measures that included scoring criteria, efficient linguistic features for writing assessment, and detailed measurement units and calculation formulas. The remaining models (Models 3 to 18) utilized GPT-4 prompted with individual measures. The performance of these 18 different models was assessed using the output indicators described in Section Criteria (output indicator) . By comparing the performances of these models, the study aimed to understand the impact of prompt engineering on the accuracy and effectiveness of GPT-4 in AES tasks.

Based on the PRMSE scores presented in Fig. 4 , it was observed that Model 1, representing GPT-4 without any additional prompting, achieved a fair level of performance. However, Model 2, which utilized GPT-4 prompted with all measures, outperformed all other models in terms of PRMSE score, achieving a score of 0.681. These results indicate that the inclusion of specific measures and prompts significantly enhanced the performance of GPT-4 in AES. Among the measures, syntactic complexity was found to play a particularly significant role in improving the accuracy of GPT-4 in assessing writing quality. Following that, lexical diversity emerged as another important factor contributing to the model’s effectiveness. The study suggests that a well-prompted GPT-4 can serve as a valuable tool to support human assessors in evaluating writing quality. By utilizing GPT-4 as an automated scoring tool, the evaluation biases associated with human raters can be minimized. This has the potential to empower teachers by allowing them to focus on designing writing tasks and guiding writing strategies, while leveraging the capabilities of GPT-4 for efficient and reliable scoring.

figure 4

PRMSE scores of the 18 AES models.

This study aimed to investigate two main research questions: the feasibility of utilizing LLMs for AES and the impact of prompt engineering on the application of LLMs in AES.

To address the first objective, the study compared the effectiveness of five different models: GPT, BERT, the Japanese local LLM (OCLL), and two conventional machine learning-based AES tools (Jess and JWriter). The PRMSE values indicated that the GPT-4-based method outperformed other LLMs (BERT, OCLL) and linguistic feature-based computational methods (Jess and JWriter) across various writing proficiency criteria. Furthermore, the agreement coefficient between GPT-4 and human scoring surpassed the agreement among human raters themselves, highlighting the potential of using the GPT-4 tool to enhance AES by reducing biases and subjectivity, saving time, labor, and cost, and providing valuable feedback for self-study. Regarding the second goal, the role of prompt design was investigated by comparing 18 models, including a baseline model, a model prompted with all measures, and 16 models prompted with one measure at a time. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. The PRMSE scores of the models showed that GPT-4 prompted with all measures achieved the best performance, surpassing the baseline and other models.

In conclusion, this study has demonstrated the potential of LLMs in supporting human rating in assessments. By incorporating automation, we can save time and resources while reducing biases and subjectivity inherent in human rating processes. Automated language assessments offer the advantage of accessibility, providing equal opportunities and economic feasibility for individuals who lack access to traditional assessment centers or necessary resources. LLM-based language assessments provide valuable feedback and support to learners, aiding in the enhancement of their language proficiency and the achievement of their goals. This personalized feedback can cater to individual learner needs, facilitating a more tailored and effective language-learning experience.

There are three important areas that merit further exploration. First, prompt engineering requires attention to ensure optimal performance of LLM-based AES across different language types. This study revealed that GPT-4, when prompted with all measures, outperformed models prompted with fewer measures. Therefore, investigating and refining prompt strategies can enhance the effectiveness of LLMs in automated language assessments. Second, it is crucial to explore the application of LLMs in second-language assessment and learning for oral proficiency, as well as their potential in under-resourced languages. Recent advancements in self-supervised machine learning techniques have significantly improved automatic speech recognition (ASR) systems, opening up new possibilities for creating reliable ASR systems, particularly for under-resourced languages with limited data. However, challenges persist in the field of ASR. First, ASR assumes correct word pronunciation for automatic pronunciation evaluation, which proves challenging for learners in the early stages of language acquisition due to diverse accents influenced by their native languages. Accurately segmenting short words becomes problematic in such cases. Second, developing precise audio-text transcriptions for languages with non-native accented speech poses a formidable task. Last, assessing oral proficiency levels involves capturing various linguistic features, including fluency, pronunciation, accuracy, and complexity, which are not easily captured by current NLP technology.

Data availability

The dataset utilized was obtained from the International Corpus of Japanese as a Second Language (I-JAS). The data URLs: [ https://www2.ninjal.ac.jp/jll/lsaj/ihome2.html ].

J-CAT and TTBJ are two computerized adaptive tests used to assess Japanese language proficiency.

SPOT is a specific component of the TTBJ test.

J-CAT: https://www.j-cat2.org/html/ja/pages/interpret.html

SPOT: https://ttbj.cegloc.tsukuba.ac.jp/p1.html#SPOT .

The study utilized a prompt-based GPT-4 model, developed by OpenAI, which has an impressive architecture with 1.8 trillion parameters across 120 layers. GPT-4 was trained on a vast dataset of 13 trillion tokens, using two stages: initial training on internet text datasets to predict the next token, and subsequent fine-tuning through reinforcement learning from human feedback.

https://www2.ninjal.ac.jp/jll/lsaj/ihome2-en.html .

http://jhlee.sakura.ne.jp/JEV/ by Japanese Learning Dictionary Support Group 2015.

We express our sincere gratitude to the reviewer for bringing this matter to our attention.

On February 7, 2023, Microsoft began rolling out a major overhaul to Bing that included a new chatbot feature based on OpenAI’s GPT-4 (Bing.com).

Appendix E-F present the analysis results of the QWK coefficient between the scores computed by the human raters and the BERT, OCLL models.

Attali Y, Burstein J (2006) Automated essay scoring with e-rater® V.2. J. Technol., Learn. Assess., 4

Barkaoui K, Hadidi A (2020) Assessing Change in English Second Language Writing Performance (1st ed.). Routledge, New York. https://doi.org/10.4324/9781003092346

Bentz C, Tatyana R, Koplenig A, Tanja S (2016) A comparison between morphological complexity. measures: Typological data vs. language corpora. In Proceedings of the workshop on computational linguistics for linguistic complexity (CL4LC), 142–153. Osaka, Japan: The COLING 2016 Organizing Committee

Bond TG, Yan Z, Heene M (2021) Applying the Rasch model: Fundamental measurement in the human sciences (4th ed). Routledge

Brants T (2000) Inter-annotator agreement for a German newspaper corpus. Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00), Athens, Greece, 31 May-2 June, European Language Resources Association

Brown TB, Mann B, Ryder N, et al. (2020) Language models are few-shot learners. Advances in Neural Information Processing Systems, Online, 6–12 December, Curran Associates, Inc., Red Hook, NY

Burstein J (2003) The E-rater scoring engine: Automated essay scoring with natural language processing. In Shermis MD and Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Čech R, Miroslav K (2018) Morphological richness of text. In Masako F, Václav C (ed) Taming the corpus: From inflection and lexis to interpretation, 63–77. Cham, Switzerland: Springer Nature

Çöltekin Ç, Taraka, R (2018) Exploiting Universal Dependencies treebanks for measuring morphosyntactic complexity. In Aleksandrs B, Christian B (ed), Proceedings of first workshop on measuring language complexity, 1–7. Torun, Poland

Crossley SA, Cobb T, McNamara DS (2013) Comparing count-based and band-based indices of word frequency: Implications for active vocabulary research and pedagogical applications. System 41:965–981. https://doi.org/10.1016/j.system.2013.08.002

Article   Google Scholar  

Crossley SA, McNamara DS (2016) Say more and be more coherent: How text elaboration and cohesion can increase writing quality. J. Writ. Res. 7:351–370

CyberAgent Inc (2023) Open-Calm series of Japanese language models. Retrieved from: https://www.cyberagent.co.jp/news/detail/id=28817

Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota, 2–7 June, pp. 4171–4186. Association for Computational Linguistics

Diez-Ortega M, Kyle K (2023) Measuring the development of lexical richness of L2 Spanish: a longitudinal learner corpus study. Studies in Second Language Acquisition 1-31

Eckes T (2009) On common ground? How raters perceive scoring criteria in oral proficiency testing. In Brown A, Hill K (ed) Language testing and evaluation 13: Tasks and criteria in performance assessment (pp. 43–73). Peter Lang Publishing

Elliot S (2003) IntelliMetric: from here to validity. In: Shermis MD, Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Google Scholar  

Engber CA (1995) The relationship of lexical proficiency to the quality of ESL compositions. J. Second Lang. Writ. 4:139–155

Garner J, Crossley SA, Kyle K (2019) N-gram measures and L2 writing proficiency. System 80:176–187. https://doi.org/10.1016/j.system.2018.12.001

Haberman SJ (2008) When can subscores have value? J. Educat. Behav. Stat., 33:204–229

Haberman SJ, Yao L, Sinharay S (2015) Prediction of true test scores from observed item scores and ancillary data. Brit. J. Math. Stat. Psychol. 68:363–385

Halliday MAK (1985) Spoken and Written Language. Deakin University Press, Melbourne, Australia

Hirao R, Arai M, Shimanaka H et al. (2020) Automated essay scoring system for nonnative Japanese learners. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 1250–1257. European Language Resources Association

Hunt KW (1966) Recent Measures in Syntactic Development. Elementary English, 43(7), 732–739. http://www.jstor.org/stable/41386067

Ishioka T (2001) About e-rater, a computer-based automatic scoring system for essays [Konpyūta ni yoru essei no jidō saiten shisutemu e − rater ni tsuite]. University Entrance Examination. Forum [Daigaku nyūshi fōramu] 24:71–76

Hochreiter S, Schmidhuber J (1997) Long short- term memory. Neural Comput. 9(8):1735–1780

Article   CAS   PubMed   Google Scholar  

Ishioka T, Kameda M (2006) Automated Japanese essay scoring system based on articles written by experts. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 17–18 July 2006, pp. 233-240. Association for Computational Linguistics, USA

Japan Foundation (2021) Retrieved from: https://www.jpf.gp.jp/j/project/japanese/survey/result/dl/survey2021/all.pdf

Jarvis S (2013a) Defining and measuring lexical diversity. In Jarvis S, Daller M (ed) Vocabulary knowledge: Human ratings and automated measures (Vol. 47, pp. 13–44). John Benjamins. https://doi.org/10.1075/sibil.47.03ch1

Jarvis S (2013b) Capturing the diversity in lexical diversity. Lang. Learn. 63:87–106. https://doi.org/10.1111/j.1467-9922.2012.00739.x

Jiang J, Quyang J, Liu H (2019) Interlanguage: A perspective of quantitative linguistic typology. Lang. Sci. 74:85–97

Kim M, Crossley SA, Kyle K (2018) Lexical sophistication as a multidimensional phenomenon: Relations to second language lexical proficiency, development, and writing quality. Mod. Lang. J. 102(1):120–141. https://doi.org/10.1111/modl.12447

Kojima T, Gu S, Reid M et al. (2022) Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, New Orleans, LA, 29 November-1 December, Curran Associates, Inc., Red Hook, NY

Kyle K, Crossley SA (2015) Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Q 49:757–786

Kyle K, Crossley SA, Berger CM (2018) The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behav. Res. Methods 50:1030–1046. https://doi.org/10.3758/s13428-017-0924-4

Article   PubMed   Google Scholar  

Kyle K, Crossley SA, Jarvis S (2021) Assessing the validity of lexical diversity using direct judgements. Lang. Assess. Q. 18:154–170. https://doi.org/10.1080/15434303.2020.1844205

Landauer TK, Laham D, Foltz PW (2003) Automated essay scoring and annotation of essays with the Intelligent Essay Assessor. In Shermis MD, Burstein JC (ed), Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159–174

Laufer B, Nation P (1995) Vocabulary size and use: Lexical richness in L2 written production. Appl. Linguist. 16:307–322. https://doi.org/10.1093/applin/16.3.307

Lee J, Hasebe Y (2017) jWriter Learner Text Evaluator, URL: https://jreadability.net/jwriter/

Lee J, Kobayashi N, Sakai T, Sakota K (2015) A Comparison of SPOT and J-CAT Based on Test Analysis [Tesuto bunseki ni motozuku ‘SPOT’ to ‘J-CAT’ no hikaku]. Research on the Acquisition of Second Language Japanese [Dainigengo to shite no nihongo no shūtoku kenkyū] (18) 53–69

Li W, Yan J (2021) Probability distribution of dependency distance based on a Treebank of. Japanese EFL Learners’ Interlanguage. J. Quant. Linguist. 28(2):172–186. https://doi.org/10.1080/09296174.2020.1754611

Article   MathSciNet   Google Scholar  

Linacre JM (2002) Optimizing rating scale category effectiveness. J. Appl. Meas. 3(1):85–106

PubMed   Google Scholar  

Linacre JM (1994) Constructing measurement with a Many-Facet Rasch Model. In Wilson M (ed) Objective measurement: Theory into practice, Volume 2 (pp. 129–144). Norwood, NJ: Ablex

Liu H (2008) Dependency distance as a metric of language comprehension difficulty. J. Cognitive Sci. 9:159–191

Liu H, Xu C, Liang J (2017) Dependency distance: A new perspective on syntactic patterns in natural languages. Phys. Life Rev. 21. https://doi.org/10.1016/j.plrev.2017.03.002

Loukina A, Madnani N, Cahill A, et al. (2020) Using PRMSE to evaluate automated scoring systems in the presence of label noise. Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, Seattle, WA, USA → Online, 10 July, pp. 18–29. Association for Computational Linguistics

Lu X (2010) Automatic analysis of syntactic complexity in second language writing. Int. J. Corpus Linguist. 15:474–496

Lu X (2012) The relationship of lexical richness to the quality of ESL learners’ oral narratives. Mod. Lang. J. 96:190–208

Lu X (2017) Automated measurement of syntactic complexity in corpus-based L2 writing research and implications for writing assessment. Lang. Test. 34:493–511

Lu X, Hu R (2022) Sense-aware lexical sophistication indices and their relationship to second language writing quality. Behav. Res. Method. 54:1444–1460. https://doi.org/10.3758/s13428-021-01675-6

Ministry of Health, Labor, and Welfare of Japan (2022) Retrieved from: https://www.mhlw.go.jp/stf/newpage_30367.html

Mizumoto A, Eguchi M (2023) Exploring the potential of using an AI language model for automated essay scoring. Res. Methods Appl. Linguist. 3:100050

Okgetheng B, Takeuchi K (2024) Estimating Japanese Essay Grading Scores with Large Language Models. Proceedings of 30th Annual Conference of the Language Processing Society in Japan, March 2024

Ortega L (2015) Second language learning explained? SLA across 10 contemporary theories. In VanPatten B, Williams J (ed) Theories in Second Language Acquisition: An Introduction

Rae JW, Borgeaud S, Cai T, et al. (2021) Scaling Language Models: Methods, Analysis & Insights from Training Gopher. ArXiv, abs/2112.11446

Read J (2000) Assessing vocabulary. Cambridge University Press. https://doi.org/10.1017/CBO9780511732942

Rudner LM, Liang T (2002) Automated Essay Scoring Using Bayes’ Theorem. J. Technol., Learning and Assessment, 1 (2)

Sakoda K, Hosoi Y (2020) Accuracy and complexity of Japanese Language usage by SLA learners in different learning environments based on the analysis of I-JAS, a learners’ corpus of Japanese as L2. Math. Linguist. 32(7):403–418. https://doi.org/10.24701/mathling.32.7_403

Suzuki N (1999) Summary of survey results regarding comprehensive essay questions. Final report of “Joint Research on Comprehensive Examinations for the Aim of Evaluating Applicability to Each Specialized Field of Universities” for 1996-2000 [shōronbun sōgō mondai ni kansuru chōsa kekka no gaiyō. Heisei 8 - Heisei 12-nendo daigaku no kaku senmon bun’ya e no tekisei no hyōka o mokuteki to suru sōgō shiken no arikata ni kansuru kyōdō kenkyū’ saishū hōkoku-sho]. University Entrance Examination Section Center Research and Development Department [Daigaku nyūshi sentā kenkyū kaihatsubu], 21–32

Taghipour K, Ng HT (2016) A neural approach to automated essay scoring. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 1–5 November, pp. 1882–1891. Association for Computational Linguistics

Takeuchi K, Ohno M, Motojin K, Taguchi M, Inada Y, Iizuka M, Abo T, Ueda H (2021) Development of essay scoring methods based on reference texts with construction of research-available Japanese essay data. In IPSJ J 62(9):1586–1604

Ure J (1971) Lexical density: A computational technique and some findings. In Coultard M (ed) Talking about Text. English Language Research, University of Birmingham, Birmingham, England

Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. In Advances in Neural Information Processing Systems, Long Beach, CA, 4–7 December, pp. 5998–6008, Curran Associates, Inc., Red Hook, NY

Watanabe H, Taira Y, Inoue Y (1988) Analysis of essay evaluation data [Shōronbun hyōka dēta no kaiseki]. Bulletin of the Faculty of Education, University of Tokyo [Tōkyōdaigaku kyōiku gakubu kiyō], Vol. 28, 143–164

Yao S, Yu D, Zhao J, et al. (2023) Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36

Zenker F, Kyle K (2021) Investigating minimum text lengths for lexical diversity indices. Assess. Writ. 47:100505. https://doi.org/10.1016/j.asw.2020.100505

Zhang Y, Warstadt A, Li X, et al. (2021) When do you need billions of words of pretraining data? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online, pp. 1112-1125. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.90

Download references

This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.

Author information

Authors and affiliations.

Department of Japanese Studies, Zhejiang University, Hangzhou, China

Department of Linguistics and Applied Linguistics, Zhejiang University, Hangzhou, China

You can also search for this author in PubMed   Google Scholar

Contributions

Wenchao Li is in charge of conceptualization, validation, formal analysis, investigation, data curation, visualization and writing the draft. Haitao Liu is in charge of supervision.

Corresponding author

Correspondence to Wenchao Li .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental material file #1, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Li, W., Liu, H. Applying large language models for automated essay scoring for non-native Japanese. Humanit Soc Sci Commun 11 , 723 (2024). https://doi.org/10.1057/s41599-024-03209-9

Download citation

Received : 02 February 2024

Accepted : 16 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1057/s41599-024-03209-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

how to write a discourse analysis essay

  • Share full article

Advertisement

Supported by

the new new world

As China’s Internet Disappears, ‘We Lose Parts of Our Collective Memory’

The number of Chinese websites is shrinking and posts are being removed and censored, stoking fears about what happens when history is erased.

An illustration of a large creature with glowing red eyes. Its paws are on stacks of paper, which are also in its mouth, in between its baring fangs. Nearby, people are holding documents, two of them holding up one that says “404.”

Chinese people know their country’s internet is different. There is no Google, YouTube, Facebook or Twitter. They use euphemisms online to communicate the things they are not supposed to mention. When their posts and accounts are censored, they accept it with resignation.

They live in a parallel online universe. They know it and even joke about it.

Now they are discovering that, beneath a facade bustling with short videos, livestreaming and e-commerce, their internet — and collective online memory — is disappearing in chunks.

A post on WeChat on May 22 that was widely shared reported that nearly all information posted on Chinese news portals, blogs, forums, social media sites between 1995 and 2005 was no longer available.

“The Chinese internet is collapsing at an accelerating pace,” the headline said. Predictably, the post itself was soon censored.

“We used to believe that the internet had a memory,” He Jiayan, a blogger who writes about successful businesspeople, wrote in the post. “But we didn’t realize that this memory is like that of a goldfish.”

It’s impossible to determine exactly how much and what content has disappeared. But I did a test. I used China’s top search engine, Baidu, to look up some of the examples cited in Mr. He’s post, focusing on about the same time frame between the mid-1990s and mid-2000s.

I started with Alibaba’s Jack Ma and Tencent’s Pony Ma, two of China’s most successful internet entrepreneurs, both of whom Mr. He had searched for. I also searched for Liu Chuanzhi, known as the godfather of Chinese entrepreneurs: He made headlines when his company, Lenovo, acquired IBM’s personal computer business in 2005.

I looked, too, for results for China’s top leader, Xi Jinping, who during the period was the governor of two big provinces. Search results of senior Chinese leaders are always closely controlled. I wanted to see what people could find if they were curious about what Mr. Xi was like before he became a national leader.

I got no results when I searched for Ma Yun , which is Jack Ma’s name in Chinese. I found three entries for Ma Huateng , which is Pony Ma’s name. A search for Liu Chuanzhi turned up seven entries.

There were zero results for Mr. Xi.

Then I searched for one of the most consequential tragedies in China in the past few decades: the Great Sichuan earthquake on May 12, 2008, which killed over 69,000 people. It happened during a brief period when Chinese journalists had more freedom than the Communist Party would usually allow, and they produced a lot of high-quality journalism.

When I narrowed the time frame to May 12, 2008, to May 12, 2009, Baidu came up with nine pages of search results, most of which consisted of articles on the websites of the central government or the state broadcaster China Central Television. One caveat: If you know the names of the journalists and their organizations, you can find more.

Each results page had about 10 headlines. My search found what had to have been a small fraction of the coverage at that time, much of which was published on the sites of newspapers and magazines that sent journalists to the epicenter of the earthquake. I didn’t find any of the outstanding news coverage or outpouring of online grief that I remembered.

In addition to disappearing content, there’s a broader problem: China’s internet is shrinking. There were 3.9 million websites in China in 2023, down more than a third from 5.3 million in 2017, according to the country’s internet regulator.

China has one billion internet users, or nearly one-fifth of the world’s online population. Yet the number of websites using Chinese language make up only 1.3 percent of the global total, down from 4.3 percent in 2013 — a 70 percent plunge over a decade, according to Web Technology Surveys , which tracks online use of top content languages.

The number of Chinese language websites is now only slightly higher than those in Indonesian and Vietnamese, and smaller than those in Polish and Persian. It’s half the number of Italian language sites and just over a quarter of those in Japanese.

One reason for the decline is that it is technically difficult and costly for websites to archive older content, and not just in China . But in China, the other reason is political.

Internet publishers, especially news portals and social media platforms, have faced heightened pressure to censor as the country has made an authoritarian and nationalistic turn under Mr. Xi’s leadership. Keeping China’s cyberspace politically and culturally pure is a top order of the Communist Party. Internet companies have more incentive to over-censor and let older content disappear by not archiving.

Many people have had their online existences erased.

Two weeks ago, Nanfu Wang found that an entry about her on a Wikipedia-like site was gone. Ms. Wang, a documentary filmmaker, searched her name on the film review site Douban and came up with nothing. Same with WeChat.

“Some of the films I directed had been deleted and banned on the Chinese internet,” she said. “But this time, I feel that I, as a part of history, have been erased.” She doesn’t know what triggered it.

Zhang Ping, better known by his pen name, Chang Ping, was one of China’s most famous journalists in the 2000s. His articles were everywhere. Then in 2011, his writing provoked the wrath of the censors.

“My presence in public discourse has been stifled much more severely than I anticipated, and that represents a significant loss of my personal life,” he told me. “My life has been negated.”

When my Weibo account was deleted in March 2021, I was saddened and angered. It had more than three million followers and thousands of posts recording my life and thoughts over a decade. Many of the posts were about current affairs, history or politics, but some were personal musings. I felt a part of my life had been carved away.

Many people intentionally hide their online posts because they could be used against them by the party or its proxies. In a trend called “grave digging,” nationalistic “little pinks” pore over past online writings of intellectuals, entertainers and influencers.

For Chinese, our online memories, even frivolous ones, can become baggage we need to unload.

“Even though we tend to think of the internet as somewhat superficial,” said Ian Johnson, a longtime China correspondent and author, “without many of these sites and things, we lose parts of our collective memory.”

In “ Sparks ,” a book by Mr. Johnson about brave historians in China who work underground, he cited the Internet Archive for Chinese online sources in the endnotes because, he said, he knew they would all eventually disappear.

“History matters in every country, but it really matters to the C.C.P.,” he said, referring to the Chinese Communist Party. “It’s history that justifies the party’s continued rule.”

Mr. Johnson founded the China Unofficial Archives website, which seeks to preserve blogs, movies and documents outside the Chinese internet.

There are other projects to save Chinese memories and history from falling into a void. Greatfire.org has several websites that provide access to censored content. China Digital Times , a nonprofit that fights censorship, archives work that has been or is in danger of being blocked. Mr. Zhang, the journalist, is its executive editor.

Mr. He, author of the WeChat post that went viral, is deeply pessimistic that China’s erasure of history can be reversed.

“If you can still see some early information on the Chinese internet now,” he wrote, “it is just the last ray of the setting sun.”

Li Yuan writes The New New World column, which focuses on China’s growing influence on the world by examining its businesses, politics and society. More about Li Yuan

IMAGES

  1. What Is a Discourse Analysis Essay: Example & Step-by-Step Guide

    how to write a discourse analysis essay

  2. ⇉Critical Discourse Analysis Essay Example

    how to write a discourse analysis essay

  3. Discourse Markers List for Essay

    how to write a discourse analysis essay

  4. Critical Discourse Analysis of speech of Obama

    how to write a discourse analysis essay

  5. What Is a Discourse Analysis Essay: Example & Step-by-Step Guide

    how to write a discourse analysis essay

  6. Critical Discourse Analysis

    how to write a discourse analysis essay

VIDEO

  1. Discourse Analysis And Critical Discourse Analysis CDA

  2. Coherence

  3. What is Critical Discourse Analysis

  4. Critical Discourse Analysis

  5. discourse analysis

  6. Discourse Analysis

COMMENTS

  1. Critical Discourse Analysis

    Critical discourse analysis (or discourse analysis) is a research method for studying written or spoken language in relation to its social context. It aims to understand how language is used in real life situations. When you conduct discourse analysis, you might focus on: The purposes and effects of different types of language.

  2. What Is a Discourse Analysis Essay: Example & Guide

    Follow our step-by-step guide, and you'll excel at it. Step #1: Choose the research question and select the content of the analysis. Coming up with a clearly defined research question is crucial. There's no universal set of criteria for a good research question. However, try to make sure that you research question:

  3. What Is Discourse Analysis? Definition + Examples

    As Wodak and Krzyżanowski (2008) put it: "discourse analysis provides a general framework to problem-oriented social research". Basically, discourse analysis is used to conduct research on the use of language in context in a wide variety of social problems (i.e., issues in society that affect individuals negatively).

  4. 21 Great Examples of Discourse Analysis (2024)

    Today, most methodology chapters in dissertations that use discourse analysis will have extensive discussions of Fairclough's methods. Conclusion. Discourse analysis is a popular primary research method in media studies, cultural studies, education studies, and communication studies. It helps scholars to show how texts and language have the ...

  5. How to Do a Critical Discourse Analysis: 11 Steps (with Pictures)

    The first author would want to tap into popular trends ends of the day in order to profit, while the second author would be less concerned with pleasing the public. 2. Examine the form of the text and consider who has access to it. Within CDA, a text's form and its audience are closely related.

  6. Critical Discourse Analysis

    How language use relates to its social, political, and historical context. Discourse analysis is a common qualitative research method in many humanities and social science disciplines, including linguistics, sociology, anthropology, psychology, and cultural studies. It is also called critical discourse analysis.

  7. Discourse Analysis

    Interpretive approach: Discourse analysis is an interpretive approach, meaning that it seeks to understand the meaning and significance of language use from the perspective of the participants in a particular discourse. Emphasis on reflexivity: Discourse analysis emphasizes the importance of reflexivity, or self-awareness, in the research process.

  8. Discourse Analysis

    Step 5: Present your Findings. It's time to present your results. Throughout the process, you gathered detailed notes of the discourse, building a strong presentation or thesis. You can use the references of other relevant sources as evidence to support your discussion.

  9. How to Write Up a Discourse Analysis

    This video explains features of a discourse analysis article that are helpful for students in learning to write about their own studies.To view the video on ...

  10. Discourse Analysis ~ Definition & How to do It

    Discourse analysis: Step-by-step. 1. Define your primary questions. If you're using discourse analysis as a research tool, you'll want to frame your research with one or two relevant research questions. This will help you stay on topic and bring coherence to your work. 2.

  11. 1b. Discourse Communities

    Considering your discourse community can give your writing its audience, context, and purpose, which are crucial for motivating your writing. In this chapter, we will: Define discourse community. Identify the various discourse communities of which you are a part. Understand how a discourse community shapes your writing.

  12. How to Write a Rhetorical Analysis

    A rhetorical analysis is a type of essay that looks at a text in terms of rhetoric. This means it is less concerned with what the author is saying than with how they say it: their goals, techniques, and appeals to the audience. A rhetorical analysis is structured similarly to other essays: an introduction presenting the thesis, a body analyzing ...

  13. Textual Analysis

    Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text - from its literal meaning to the subtext, symbolism, assumptions, and values it reveals. The methods used to conduct textual analysis depend on the field and the aims of the research.

  14. How to Do a Discourse Analysis

    A toolbox for analysing political texts. Discourse analysis is a useful tool for studying the political meanings that inform written and spoken text. In other posts, I have provided a quick video introduction to the topic, and have discussed the ideas behind discourse theory, the main questions that students and researchers will likely ask as they set up their discourse analysis project, and ...

  15. NROC Developmental English Foundations

    An analysis is the end result of analyzing. of a text Words that make up a book, essay, article, poem, or speech. through an exam; however, you may also be required to write an essay A short piece of writing that focuses on at least one main idea. Some essays are also focused on the author's unique point of view, making them personal or ...

  16. Critical Discourse Analysis

    One of the most significant concerns of critical discourse analysis involves developing an understanding of the relationship between languages, dominance, and social power. Such an understanding helps in predicting the contribution of discourse on the reproduction of various power differences. We will write.

  17. Written discourse

    Essays marked with a * received a distinction. * Analyzing and raising students' awareness of textual patterns in authentic texts: Mohammad Umar Farooq. Written Text Analysis: Gregory S. Hadley. *Show an analysis of the whole text in terms of the main underlying text pattern. Identify the signals that indicate this pattern David Evans.

  18. Essays on Discourse Community

    The concept of discourse community emerged as a framework in the field of sociolinguistics and discourse analysis. Although there is no specific historical origin attributed to it, the study of discourse communities can be traced back to the works of scholars such as John Swales and James Gee in the late 20th century. ... Writing an essay on ...

  19. How to Write a Discursive Essay: Tips to Succeed & Examples

    Start with an introduction to the topic. Discuss each essay question in a single paragraph. Begin each paragraph with a powerful issue sentence. Paragraphs with one point usually followed by a counterpoint paragraph. Its style is general for essays as the reader should understand what you stand for.

  20. How to Write a Literary Analysis Essay

    Table of contents. Step 1: Reading the text and identifying literary devices. Step 2: Coming up with a thesis. Step 3: Writing a title and introduction. Step 4: Writing the body of the essay. Step 5: Writing a conclusion. Other interesting articles.

  21. Printing a New Age of Design: A Rhetorical Analysis of Neri Oxman's TED

    His essay investigates the different rhetorical techniques employed by Oxman in order to promote biological additive manufacturing, which may prove to be a viable solution to many design problems. In the future, Jason hopes to find a job within the automotive industry, specifically focusing on design.

  22. Everything You Should Know About Academic Writing ...

    Academic writing is a structured and evidence-based form of writing that is used to convey scholarly information. It includes essays, research papers, dissertations, and reports. This writing style emphasises clarity, precision, and a logical flow of ideas, often incorporating a formal tone and style.

  23. Applying large language models for automated essay scoring for non

    Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated ...

  24. As China's Internet Disappears, 'We Lose Parts of Our Collective Memory

    In addition to disappearing content, there's a broader problem: China's internet is shrinking. There were 3.9 million websites in China in 2023, down more than a third from 5.3 million in 2017 ...