critical discourse analysis research method

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• Critical Discourse Analysis | Definition, Guide & Examples

Critical Discourse Analysis | Definition, Guide & Examples

Published on August 23, 2019 by Amy Luo . Revised on June 22, 2023.

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
• 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.  

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

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

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Unlike linguistic approaches that focus only on the rules of language use, discourse analysis emphasizes 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 analyzed on multiple levels.

Discourse analysis is a qualitative and interpretive method of analyzing 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. Following these steps can help you avoid pitfalls of confirmation bias that can cloud your analysis.

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: Analyze 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.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Luo, A. (2023, June 22). Critical Discourse Analysis | Definition, Guide & Examples. Scribbr. Retrieved April 15, 2024, from https://www.scribbr.com/methodology/discourse-analysis/

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• 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 15 April 2024, from https://www.scribbr.co.uk/research-methods/discourse-analysis-explained/

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How to Do Critical Discourse Analysis A Multimodal Introduction

• David Machin - Cardiff University, UK
• Andrea Mayr - Zayed University, UAE
• Description

Want to use Critical Discourse Analysis, but not sure where to start? This book is the complete toolkit you need.

Each chapter presents distinct concepts and ideas in Critical Discourse Analysis, explaining how to use them in your research – and why. Packed with case studies of news texts, social media content, memes, promotional videos, institutional documents, infographics and webpages, the book shows you how to apply each set of tools to real life examples.

Most importantly, examples, case studies, and revised opening chapters of this second edition show how multimodal Critical Discourse Analysis can help us to understand the role of language in the functioning of society and politics today, shaping our priorities for what to do and how to act.

This book is an inspiring and valuable resource for any undergraduate students and researchers who wish to understand and use Critical Discourse Analysis.

David Machin is Professor of Linguistics at Shanghai International Studies University.

Andrea Mayr is an Associate Professor at Zayed University.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

It it a fantastic book that can be used in an advance research method module, In depth discussion about CDA and frameworks on how to apply it.

This is a practical, applied text that I consider essential for students pursuing communications studies, including those specialising in the visual arts - where the power of text tends to be ignored in favour of the visual turn.

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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 88,461 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

• 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.

• 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.”

• 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.

• 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

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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

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• ↑ 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

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Home » Discourse Analysis – Methods, Types and Examples

Discourse Analysis – Methods, Types and Examples

Table of Contents

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.

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Corpus-Based Critical Discourse Analysis: Data and Research Methods

• First Online: 01 January 2023

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• Yuxi Wu 2  

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In the previous chapter, the theoretical framework of this study was introduced. This chapter describes the methods adopted in the present study. It begins with the research question ( 3.1 ), and data description followed ( 3.2 ), which gives an account of the news data used in this study, including the background of the newspapers in the three territories as well as their respective ideologies and stances. This is followed by corpus compilation ( 3.3 ), which describes the news article corpora. The analytical frameworks for data analysis are described in 3.4 , and the research design ( 3.5 ) represents the three-step research procedures. The two corpus tools, Wordsmith 7.0 (Scott, 2016 ) and Wmatrix (Rayson, 2008 ), are used to investigate the data in the three news article corpora.

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3.1 Research Question

The research question addressed in this study is:

How do English-language newspapers published in Mainland China, Hong Kong SAR, and Macau SAR compare in terms of their representations of Macau’s gaming industry?

The comparison of media representations of Macau’s gaming industry is achieved through the three corpus methods, namely frequent single lexical words and co-occurrences of these frequent words based on the Sinclair’s ( 2004 ) model of five categories of co-selection of a lexical item, trigrams, and key semantic fields (Rayson, 2008 ) generated by comparing each news article corpus with the same reference corpus BNC written Sampler. This chapter describes the research methods in the present study.

3.2 Data Description: Introducing Four Newspapers in Chinese Mainland, Hong Kong, and Macau

The data analysed in the study were collected from four English-language newspapers from three territories in China, namely Mainland China, Hong Kong, and Macau. Although these three places belong to the territory of the People’s Republic of China (PRC), they have different historical backgrounds, economic statuses, and political systems. (see Sect. 1.3.2 ).

Newspaper articles published from 1 January 2014 to 31 December 2014 were collected from four English newspapers as the year of 2014 is the very special year for Macau (see Sect. 1.3.1 ). They were China Daily ( CD ) and Xinhua News Agency ( XH ) from Chinese Mainland, South China Morning Post ( SCMP ) from Hong Kong and Macau Post Daily ( MPD ) from Macau. These four newspapers are part of the mainstream news media in their respective regions. Both Xinhua News Agency and China Daily are state-run news media in Mainland China. Xinhua News Agency is the largest local domestic information news provider and its core value is to “publicize China and Report the World” in an “Authoritative Voice” (Xinhuanet, n.d. ). In addition to providing news releases in Chinese version, Xinhua also provides versions of six foreign languages, namely English, Portuguese, Spanish, Arabic, Russian and French. According to Scollon ( 2000 ), Xinhua News Agency is the principal official source provider where “many news stories are published in the newspaper of the Chinese government, such as People’s Daily and China daily .” (p. 765). As Xin ( 2012 ) noted, Xinhua News Agency is considered to be one of the most influential national news organizations of China and is regarded as the royal propaganda machine of the Chinese Communist Party (CCP) and the Chinese authorities. Xinhua News can represent the dominant ideology of CCP and “set an official tone for other news media” (Xin, 2012 , p. 135). China Daily , established in 1981, is the largest national English-language newspaper in Mainland China. According to Li ( 2009 ), China Daily can be treated as the English version of People’s Daily , serving as the ‘official mouthpiece’ of the Chinese Communist Party (CCP). China Daily is an English-language newspaper, primarily for non-Chinese readers and therefore it plays a role in promoting Chinese national images and expressing the Chinese government’s politics and foreign policy concerns (Li, 2009 ), and aims to represent the Chinese authorities for external communication (Liu, 2017 ).

It is noted that the ideology and stance of Xinhua News is consistent with China Daily as the Mainland Chinese media industry is still strictly controlled by the Chinese authorities. Thus, a strict censorship system is applied in Mainland China (Frisch et al., 2017 ).

South China Morning Post ( SCMP ) has been the leading English newspaper in Hong Kong since 1903. The SCMP is characterized by the elite readership (Cheng & Lam, 2010 ; Flowerdew et al., 2002 ). The SCMP identifies itself as the most influential paper for Elite Business readers in Hong Kong (South China Morning Post, 2017) and 68% of its readers are university educated or above (Hong Kong Audience Survey, 2016). The reporting angles of the SCMP are believed to follow “the lead of western-based news agencies that reflects largely the views from Washington DC” (Chan, 2000 , p. 324). As Flowerdew et al. ( 2002 ) suggested, the editorial stances of the SCMP tend to be more democratic and liberal, as reflected in their stances towards the right-of-abode issue. The SCMP is different from the state-owned news media in Mainland China, which is a corporate own media. Although the SCMP has undergone several ownership shifts, its reporting angle tends to be like western-style news. (Cheng & Lam, 2010 ). It is noted that ownership of the SCMP was bought by Alibaba group in 2015 and some concerns have been expressed towards the shifts of stances of the SCMP (Frisch et al., 2017 ). However, the news articles used in this present study were collected in 2014, which will not affect the previous stances held by the SCMP .

In Macau, The Macau Post Daily ( MPD ) was founded in 2004 and is the oldest local English newspaper with the largest circulation in Macau (The Macau Post Daily, n.d ). MPD is owned by Macau Everbright Publishing, Editing & Consultancy Company Limited. (Government Information Bureau of the MSAR, n.d .). The major readers of MPD are the increasingly growing English business professionals working in Macau (Moody, 2008 ). Its director is Harald Christian Brüning, who has been working in some international agencies and MPD aims to convey Macau’s perspective. As for the ideologies and stances of MPD , to date, no academic research study has been conducted. The websites of MPD demonstrate that the editorial principles are to “support the principle of One Country, Two Systems, which forms the constitutional basis for Macau's autonomous status as an integral part of China” (The Macau Post Daily, n.d ).

3.3 Corpus Compilation and News Article Corpora

Newspaper articles published in the four newspapers were retrieved from two online databases, Wisenews and Factiva , by searching for the following items: ‘Macau’ OR ‘Macao’ AND ‘gaming’ OR ‘gambling’. The types of news articles were not limited to news reports and editorials; there were also featured stories. News articles from Xinhua News Agency (XH) and China Daily (CD) were merged due to the relatively small number of news articles related to Macau’s gaming industry in 2014. The corpus as a whole comprises 199,005 tokens: XH&CDC (31,247 tokens), MPDC (112,373 tokens), and SCMPC (55,385 tokens). As a result, three specialized news article corpora of three territories, characterised by diverse ideologies and stances, were compiled. According to McEnery et al. ( 2006 ), specialized corpora are very helpful to discourse analysts. As reviewed in Chap. 2 , many researchers have analysed different types of specialised corpora for different purposes (e.g., Baker et al., 2013 ; Cheng & Lam, 2010 , 2013 ).

The composition of the three news article corpora is described below (Table 3.1 and Fig. 3.1 ).

The number of news articles collected from the three territories

As shown in Table 3.1 and Fig. 3.1 , the number of news articles published in 2014 on the topic of Macau’s gaming industry across the three territories is not balanced. The occurrence of news articles in the MPD C (66.75%) is the highest of the three news article corpora. It is not surprising that The Macau Post Daily , as Macau’s regional press, puts its focus on news events closely related to Macau’s gaming. As mentioned before, the gaming industry significantly influences different aspects of Macanese society, such as economy, employment rate, and so on. According to McCartney ( 2015 ), Macau has become the global leader in the gaming industry in terms of gross gaming revenue since 2006. Thus, the predominant number of news articles in the MPD C is expected. By comparison, the XH & CDC only contains of 9.1% news articles in total. In Hong Kong, the occurrence of news articles concerning Macau’s gaming published in the SCMP is about three times (24.17%) higher than that in Mainland China.

3.4 Corpus Linguistics Methods

To address the research question, three corpus methods of CL were initially utilised to examine the language patterns in each news article corpus, as these methods can indicate to the researcher the appropriate starting point of the study, and can also be used to identify the significant distinctive linguistic features of respective news article corpora for comparative purposes.

CL has been widely and effectively used to trace differences and quantify linguistic elements among different newspapers (Gabrielatos & Baker, 2008 ). In this study, various methods of corpus linguistics were adopted to make sense of the linguistic patterns observed. They were the frequencies of occurrence of single lexical words, trigrams, and key semantic fields.

3.4.1 Single Word and Trigram Lists

Examining the frequency lists is considered to be a good starting point for any kinds of corpus studies and helpful in comprehending a corpus (Baker, 2006a ). A frequency list can reveal the characteristics of a corpus or text through the repeated occurrences, indicating the explicit lexical preference since “choices of words and phrases are not neutral, but communicate ideology and culture” (Brindle, 2016a , p. 43). A frequency list serves as the filter to narrow the scope of the data for further analysis.

Corpus tools such as Wordsmith 7.0 (Scott, 2016 ) and Wmatrix (Rayson, 2008 ) do not only display raw frequencies but also relative frequencies. Raw frequencies in different-sized corpora cannot be compared directly as “these raw frequencies mean very little relative to each other” (Evison, 2010 , p. 126), and therefore the technique of normalization is needed. In the present study, relative frequencies were examined for comparative purposes. In addition to the comparison of relative frequencies, the comparing of the rank order of linguistic items in more than two corpora helped to find out the difference of certain words and topics between corpora (Freake et al., 2011 ).

The study examined the most frequent single words in each news article corpus as they indicate the dominant topics and ideas of the particular corpus. Trigrams were also analysed. The importance of multi-word units, or clusters, chunks, n-grams, or lexical bundles, is well acknowledged in lexical studies. Biber ( 2006 , p. 134) defines clusters as “lexical bundles which are the most frequently occurring sequences of words”. According to Baker ( 2017 ), the frequency of clusters of more than 2 words tends to fall dramatically as length expands. Specifically, “the statistical significance of word clusters is usually measured by their recurring rate; for example, 5 or 10 occurrences in a million words.” (McEnery & Xiao, 2011 , p. 151).

3.4.2 Extended Units of Meaning of the Lexical Item

The notion of the lexical item (Sinclair, 2004 ) is central to the present study. According to Sinclair ( 2004 ), the term ‘lexical item’, originated from the ‘idiom principle’ (Sinclair, 1991 ), is considered to be an abstract category, different from the word, which needs to take meaning into consideration as language is all about producing meaning and the nature of language tends to be phraseological (Sinclair, 2004 ). The concept of ‘lexical item’ refers to “the apparently simultaneous choice of two words” (Sinclair, 1991 , p. 110). It was then developed into, defined by Sinclair’s ( 2004 ) ‘phraseological tendency’ referring to “tendency towards idiomaticity, where words tend to go together and make meaning by their combinations” (p. 29). The model of five categories of co-selection of the lexical item (Sinclair, 2004 ) has been introduced in Chap. 1 .

The present study investigated and compared the extended units of meaning of the frequent single lexical words in the three news article corpora. By examining collocation, semantic preference, and semantic prosody, the study aims to identify topical concerns and attitudes in different news article corpora.

3.4.3 Key Semantic Field Analysis

In addition to manual examination of the semantic preferences (Sinclair, 2004 ) of specific lexical items in their concordances, the study investigated entire corpora in terms of key semantic fields. The semantic tagging adopted is based on the USAS system ( http://urcel.lancs.ac.uk/usas/ ). As described by Rayson et al. ( 2004 ), USAS refers to the “UCREL semantic analysis system” and the tag sets were originally based on the Longman Lexicon of Contemporary English (McArthur, 1981 ).

The online interface Wmatrix (Rayson, 2008 ) can undertake semantic tagging automatically in the running texts. The USAS tagger function in Wmatrix can assign the semantic tagger to each word or multiword expression in the corpus to gather these words into semantic field (Rayson, 2008 ). The USAS tagset is:

hierarchical, including 21 major higher-order tags (see Table 3.2 ), labelled with letters of the alphabet, but having finer divisions marked with subsequent letters, numbers, decimal points and arithmetic symbols (Baker, 2017 , p.177).

The 21 major discourse fields are further divided into over 230 category labels (Ancher et al., 2002 ). Baker ( 2017 ) explained that “some words are tagged with one or more plus or minus signs depending on whether their meaning is generally positive or negative” (p. 178). It is noted that the USAS semantic tagger has an accuracy rate of 91–92%, suggesting the results need to be checked by the researcher carefully. According to Baker ( 2017 ), the tagger system tends to work better in the literal sense, requiring researchers to identify the metaphorical use or the mistagged case through reading concordance lines. Baker ( 2017 ) exemplifies that the word honey in the television corpus was wrongly tagged as F1 (FOOD) by the USUA. In fact, this word comes from the characters in the film who call others ‘honey’, instead of referring to food.

The process of semantic annotation is necessary for the present study. Firstly, semantic annotation can demonstrate the semantic relations between items within a corpus or the semantic features of words in a corpus (Cheng, 2012 ) and it can group words with similar meanings together and thus gaining more analysis possibilities (Potts, 2016 ). Baker and McEnery ( 2015 ) suggest that semantic tagging is a helpful feature of data down-sampling, which enables the researcher to narrow down the scale of texts and to focus on relatively small parts with predominant features. Secondly, semantic annotation is suitable for the comparative purpose. Rayson ( 2008 ) states that the advantages of comparison at the semantic level are in multi-word expressions and semantic field analysis, which are effective in illuminating and comparing the broad themes in the discourse. Thus, enriched perspectives concerning the themes in the news article corpora can be gained through the semantic field analysis.

Key semantic field analysis is based on the semantic annotation, adding the notion of ‘keyness’ to the semantic fields. Specifically, in the Wmatrix program (Rayson, 2008 ), a log likelihood value of ≥7 is statistically significant for p < 0.01(1 in 100 likelihood that the result could occur randomly). Specifically, a semantic field in the running text with a log likelihood value of ≥7 indicates it is a ‘key semantic field’ (O’Halloran, 2017 ). As explained by Rayson ( 2008 ), a key semantic field with the highest level of LL value indicates that this semantic field has the most significant difference between the study corpus and the reference corpus. Thus, LL statistical measure can illuminate the statistical significance of the study corpus when it is compared with the reference corpus.

In this study, the study corpora are three news article corpora, and each was compared with the same reference corpus BNC Sampler (Written), resulting in three lists of key semantic fields measured by LL value, which are statistically salient with regard to the reference corpus. BNC Sampler was chosen because it is regarded as the baseline corpus to identify the general tendency of each news article corpus. Since the present study focuses on written materials, namely, news articles, the BNC Sampler (Written) (1 million words), which only contains written texts, was chosen to be the reference corpus.

3.5 Research Design

A combination of the research approaches of CL and CDA is more productive than the mere utilisation of a single method (Baker, 2006a ). By analysing frequent lexical and semantic patterns and interpreting them in the broad historical, social and ideological context, the study reveals the media representations concerning Macau’s gaming industry in 2014 in three news article corpora.

Two corpus tools Wordsmith 7.0 (Scott, 2016 ) and Wmatrix (Rayson, 2008 ) were employed to provide the linguistic evidence for data analysis.

As a first step, the Wordlist function in the Wordsmith Tool 7.0 (Scott, 2016 ) was utilised to generate single lexical word frequency lists and the lists of trigrams in three news article corpora (i.e., XH&CDC , SCMPC and MPDC ). The Wordlist function of Wordsmith (Scott, 2016 ) not only produces the wordlist of a single word but also automatically identifies 2-word n-grams, 3-word n-grams and 4-word n-grams. The stop list was applied to discard 50 grammatical words, including determiner, conjunctions, prepositions etc. (e.g. the , of , and , to , in , a ) in the frequency lists. Grammatical words always occur at the top of the frequency list (Cheng, 2012 ). The function of stop list can reduce the size of the list and generate lexically-rich word lists. Numerous grammatical words can reveal the style of data and grammatical patterns (Brindle, 2016a ), which are not the focus of the present study.

The top 20 lexical words in each news article corpus were examined and compared with each other in terms of the relative frequency due to the different sizes of the three corpora. The frequency of the lexical words can then reveal the topics and ideas most frequently presented in each corpus, showing “the ideological position of the writer whose texts were utilised to compile the data” (Brindle, 2016a , p. 44) related to Macau’s gaming industry in 2014. Apart from comparing frequencies of occurrence, the co-selection patterns of the shared single lexical words and top unique words in each corpus were examined.

In the second step, the Concord function of Wordsmith Tool 7.0 (Scott, 2016 ) was used to generate the collocates and concordance lines of the most frequent 20 single lexical words. Each lexical word was then analysed manually in terms of the extended units of meaning (Sinclair, 2004 ) through the expanded concordance lines. The word span for the collocates was set at five words to the left and to the right of the node to identify words which have a relationship with each other (Baker et al., 2013 ).

As a final step, Wmatrix (Rayson, 2008 ) was utilized to obtain the top 20 key semantic fields in each news article corpus, compared with the same reference corpus BNC Sampler (Written). The top key 20 semantic fields with the largest LL values in each of the three news article corpora were investigated and then compared with each other in order to identify similarities and differences, if any, among the three corpora at the semantic level.

CDA was then used to interpret and explain the findings, taking the historical, socio-cultural, economic and political context of each region into account.

3.6 Summary

The topics of the news articles collected from four English newspapers are related to Macau’s gaming in 2014. Therefore, the examination and comparison of the frequent single lexical words and their co-selections, trigrams and key semantic fields among the three news article corpora provide comprehensive findings of the respective reporting focuses, preferences and attitudes of newspapers published in 2014 in the three territories towards Macau’s gaming industry.

Archer, D., Wilson, A., & Rayson, P. (2002). Introduction to the USAS Category System. Benedict Project Report. Retrieved, from ucrel.lancs.ac.uk/usas/usas%20guide.pdf.

Google Scholar  

Baker, P. (2006). Using corpora in discourse analysis . London: Continuum.

Book   Google Scholar  

Baker, P. (2017). American and British english: Divided by a common language? Cambridge: Cambridge University Press.

Baker, P., Gabrielatos, C., & McEnery, T. (2013). Discourse analysis and media attitudes: The representation of Islam in the British press . Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511920103

Baker, P., & McEnery, T. (2015). Introduction. In P. Baker & T. McEnery (Eds.), Corpora and discourse studies: Integrating discourse and corpora (pp. 1–20). London: Palgrave.

Chapter   Google Scholar  

Biber, D. (2006). University language: A corpus-based study of spoken and written registers . The Netherlands: John Benjamins Publishing.

Brindle, A. (2016). The language of hate: A corpus linguistic analysis of white supremacist language . London: Routledge.

Chan, Y. Y. (2000). The English-language media in Hong Kong. World Englishes, 19 (3), 323–335.

Article   Google Scholar  

Cheng, W. (2012). Exploring corpus linguistics: Language in action . New York: Routledge.

Cheng, W., & Lam, P. W. Y. (2010). Media discourses in Hong Kong: Change in representation of human rights. Text & Talk, 30 (5), 507–527.

Cheng, W., & Lam, P. W. Y. (2013). Western perceptions of Hong Kong ten years on: A corpus-driven critical discourse study. Applied Linguistics, 34 (2), 173–190.

Evison, J. (2010). What are the basics of analysing a corpus? In A. O’Keeffe & M. Michael (Eds.), The Routledge handbook of corpus linguistics (pp. 122–135). London: Routledge.

Flowerdew, J., Li, D. C., & Tran, S. (2002). Discriminatory news discourse: Some Hong Kong data. Discourse & Society, 13 (3), 319–345.

Freake, R., Gentil, G., & Sheyholislami, J. (2011). A bilingual corpus-assisted discourse study of the construction of nationhood and belonging in Quebec. Discourse & Society, 22 (1), 21–47.

Frisch, N., BelairGagnon, V., & Agur, C. (2018). Media capture with Chinese characteristics: Changing patterns in Hong Kong’s news media system. Journalism, 19 (8), 1165–1181.

Gabrielatos, C., & Baker, P. (2008). Fleeing, sneaking, flooding: A corpus analysis of discursive constructions of refugees and asylum seekers in the UK press, 1996–2005. Journal of English Linguistics, 36 (1), 5–38.

Government Information Bureau of the MSAR. (n.d.). Retrieved, from https://www.gcs.gov.mo/index.php?PageLang=E&PassType=PUB&PageCode=PUB

Li, J. (2009). Intertextuality and national identity: Discourse of national conflicts in daily newspapers in the United States and China. Discourse & Society, 20 (1), 85–121.

Liu, M. (2017). Contesting the cynicism of neoliberalism. Journal of Language and Politics, 16 (2), 242–263.

McArthur, T. (1981). Longman lexicon of contemporary English . London: Longman.

McCartney, G. (2015). When the eggs in one basket all cracked: Addressing the downturn in Macau's casino and VIP junket system . Gaming Law Review and Economics, 19 (7), 527-537.

McEnery, T., & Xiao, R. (2011). What corpora can offer in language teaching and learning. In E. Hinkel (Ed.), Handbook of research in second language teaching and learning (pp. 364–380). London: Routledge.

McEnery, T., Xiao, R., & Tono, Y. (2006). Corpus-based language studies: An advanced resource book . London: Routledge.

Moody, A. (2008). Macau English: Status, functions and forms . English today, 24 (3), 3-15.

O’Halloran, K. (2017). Posthumanism and deconstructing arguments: Corpora and digitally-driven critical analysis . New York: Taylor & Francis.

Potts, A. (2016). Semantic annotation. In P. Baker & J. Egbert (Eds.), Triangulating methodological approaches in corpus linguistic research (pp. 57–72). London: Routledge.

Rayson, P. (2008). From key words to key semantic domains. International Journal of Corpus Linguistics, 13 (4), 519–549.

Rayson, P., Archer, D., Piao, S., & McEnery, A. M. (2004). The UCREL semantic analysis system.

Scollon, R. (2000). Generic variability in news stories in Chinese and English: A contrastive discourse study of five days’ newspapers. Journal of Pragmatics, 32 (6), 761–791.

Scott, M. (2016). WordSmith Tools (Version 7) [Computer software] . Stroud: Lexical Analysis Software.

Sinclair, J. (1991). Corpus, concordance, collocation . Oxford: Oxford University Press.

Sinclair, J. (2004). Trust the text: Language, corpus and discourse . London and New York: Routledge.

The Macau Post Daily. (n.d.). Introduction . Retrieved, from http://www.macaupostdaily.com

Xin, X. (2012). How the market is changing China’s news: The case of Xinhua news agency . Maryland: Lexington Books.

Xinhuanet. (n.d.). Brief Introduction to Xinhuanet . Retrieved, from http://www.xinhuanet.com/english/special/2015-09/06/c_134594253.htm

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Wu, Y. (2023). Corpus-Based Critical Discourse Analysis: Data and Research Methods. In: Media Representations of Macau’s Gaming Industry in Greater China. Springer, Singapore. https://doi.org/10.1007/978-981-19-7724-4_3

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