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Content Analysis | A Step-by-Step Guide with Examples

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

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers, and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group, or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analysing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

• The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
• The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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Content Analysis – Methods, Types and Examples

Table of Contents

Content analysis is a widely used research technique that systematically examines and interprets textual, visual, or multimedia content to identify patterns, themes, and meanings. It is a cornerstone method in qualitative research but can also be employed quantitatively to measure the frequency of certain elements within data. This article explores the definition, methods, types, and examples of content analysis, highlighting its importance and applications across various fields.

Content Analysis

Content analysis is a research method used to analyze, categorize, and interpret the content of communication in a systematic and replicable manner. It involves breaking down material—such as text, images, or audio—into manageable data categories, often to identify trends, patterns, or underlying themes.

For example, a researcher analyzing political speeches might use content analysis to quantify how often certain keywords, like “freedom” or “equality,” are used and interpret their significance in shaping public opinion.

Key Features of Content Analysis

• Systematic Approach: Content analysis involves clearly defined rules and procedures to ensure consistency and replicability.
• Flexible Data Sources: It can analyze a variety of content types, including written documents, video recordings, and social media posts.
• Dual Purpose: It serves both qualitative purposes (understanding themes) and quantitative purposes (measuring frequency or volume).

Importance of Content Analysis

Content analysis plays a significant role in research for the following reasons:

• Understanding Communication: It helps researchers explore the meaning, structure, and function of communication.
• Tracking Trends: Content analysis is useful for monitoring changes in cultural norms, public opinion, or market behavior over time.
• Cross-Disciplinary Applications: This method is used in various fields, including sociology, marketing, media studies, and psychology.

Types of Content Analysis

1. qualitative content analysis.

Qualitative content analysis focuses on understanding the underlying themes, patterns, and meanings within a dataset. It is interpretative in nature, often exploring how content conveys emotions, opinions, or values.

For example, analyzing customer reviews to identify recurring sentiments about a product, such as satisfaction or dissatisfaction.

2. Quantitative Content Analysis

Quantitative content analysis involves counting the frequency of specific elements, such as words, phrases, or symbols, within a dataset. This type of analysis is used to quantify content trends.

For instance, studying how often particular political ideologies are mentioned in news articles during an election cycle.

3. Summative Content Analysis

Summative analysis combines both qualitative and quantitative approaches. It starts with quantitative counting and progresses into qualitative interpretation, providing a richer understanding of the context.

For example, counting mentions of “sustainability” in corporate reports and then examining how the term is used to frame environmental initiatives.

4. Relational Content Analysis

Relational analysis explores relationships between concepts, phrases, or themes in a text. It identifies connections and assesses how ideas are interrelated within the content.

For instance, analyzing a novel to determine how often two characters are mentioned together and what this implies about their relationship.

Methods of Conducting Content Analysis

1. define research questions and objectives.

Clearly articulate what you aim to discover through content analysis. For example, a marketing researcher might ask: “How do customers describe our brand on social media?”

2. Select Data Sources

Choose appropriate content sources, such as books, social media posts, videos, or interviews, depending on the research objectives.

3. Develop a Coding Framework

Establish categories and codes to classify data systematically. Codes can be predefined (deductive approach) or generated from the data itself (inductive approach).

4. Analyze Data

• Quantitative Approach: Count the frequency of codes or themes.
• Qualitative Approach: Interpret the significance of patterns and relationships.

5. Interpret Results

Evaluate findings in the context of the research questions, identifying key insights, trends, or patterns.

Steps in Content Analysis

• Data Preparation: Gather and organize the content to be analyzed.
• Coding: Segment data into meaningful categories or codes.
• Categorization: Group similar codes into broader themes.
• Analysis: Examine the data for trends, patterns, or relationships.
• Validation: Ensure reliability by double-checking the coding process or using multiple coders.
• Reporting: Present findings in a structured format, such as tables, graphs, or narratives.

Examples of Content Analysis

Example 1: social media analysis.

A business analyzing customer feedback on Twitter might use content analysis to identify common themes, such as product satisfaction, customer service complaints, or brand loyalty.

Example 2: Political Campaigns

Researchers studying election campaigns might examine speeches, advertisements, or social media posts to determine the frequency of keywords like “progress” or “change” and interpret their appeal to voters.

Example 3: Academic Research

A scholar analyzing gender representation in children’s books might classify characters based on gender roles and count their frequency to highlight disparities.

Example 4: Market Research

Content analysis of customer reviews on e-commerce platforms can reveal recurring themes, such as product durability, value for money, or delivery experiences.

Advantages of Content Analysis

• Versatility: Applicable to diverse data types, including text, visuals, and multimedia.
• Non-Intrusive: Uses pre-existing data, eliminating the need for direct interaction with subjects.
• Quantitative and Qualitative Integration: Combines numerical and interpretative insights.
• Rich Insights: Provides an in-depth understanding of communication patterns and underlying themes.

Disadvantages of Content Analysis

• Time-Intensive: Coding and analyzing large datasets can be laborious.
• Subjectivity in Interpretation: Qualitative content analysis is prone to bias, especially if coding frameworks are inconsistent.
• Limited Context: Analyzing isolated content may overlook broader contextual factors.
• Over-Reliance on Frequency: Quantitative content analysis may prioritize volume over significance.

Applications of Content Analysis

• Media Studies: Analyzing news articles or advertisements to identify biases, trends, or representations.
• Marketing: Exploring customer feedback to understand brand perception and preferences.
• Health Communication: Evaluating public health campaigns to determine their effectiveness in raising awareness.
• Education: Studying educational materials to assess inclusivity or curriculum focus.
• Sociology: Investigating societal attitudes by examining cultural artifacts, such as films, books, or songs.

Content analysis is a versatile and powerful research method for examining communication and extracting meaningful insights. By categorizing and interpreting data systematically, researchers can uncover patterns and trends across diverse fields, from media and marketing to sociology and education. While it requires careful planning and execution, the ability to analyze and interpret both qualitative and quantitative aspects of content makes it an invaluable tool for academic and practical applications.

• Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). SAGE Publications.
• Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). SAGE Publications.
• Weber, R. P. (1990). Basic Content Analysis (2nd ed.). SAGE Publications.
• Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing , 62(1), 107-115.
• Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research , 1(2).

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How to do a content analysis

What is content analysis?

Why would you use a content analysis, types of content analysis, conceptual content analysis, relational content analysis, reliability and validity, reliability, the advantages and disadvantages of content analysis, a step-by-step guide to conducting a content analysis, step 1: develop your research questions, step 2: choose the content you’ll analyze, step 3: identify your biases, step 4: define the units and categories of coding, step 5: develop a coding scheme, step 6: code the content, step 7: analyze the results, frequently asked questions about content analysis, related articles.

In research, content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. Simply put, content analysis is a research method that aims to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data , depending on the specific use case.

As such, some of the objectives of content analysis include:

• Simplifying complex, unstructured content.
• Identifying trends, patterns, and relationships in the content.
• Determining the characteristics of the content.
• Identifying the intentions of individuals through the analysis of the content.
• Identifying the implied aspects in the content.

Typically, when doing a content analysis, you’ll gather data not only from written text sources like newspapers, books, journals, and magazines but also from a variety of other oral and visual sources of content like:

• Voice recordings, speeches, and interviews.
• Web content, blogs, and social media content.
• Films, videos, and photographs.

One of content analysis’s distinguishing features is that you'll be able to gather data for research without physically gathering data from participants. In other words, when doing a content analysis, you don't need to interact with people directly.

The process of doing a content analysis usually involves categorizing or coding concepts, words, and themes within the content and analyzing the results. We’ll look at the process in more detail below.

Typically, you’ll use content analysis when you want to:

• Identify the intentions, communication trends, or communication patterns of an individual, a group of people, or even an institution.
• Analyze and describe the behavioral and attitudinal responses of individuals to communications.
• Determine the emotional or psychological state of an individual or a group of people.
• Analyze the international differences in communication content.
• Analyzing audience responses to content.

Keep in mind, though, that these are just some examples of use cases where a content analysis might be appropriate and there are many others.

The key thing to remember is that content analysis will help you quantify the occurrence of specific words, phrases, themes, and concepts in content. Moreover, it can also be used when you want to make qualitative inferences out of the data by analyzing the semantic meanings and interrelationships between words, themes, and concepts.

In general, there are two types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions. With that in mind, let’s now look at these two types of content analysis in more detail.

With conceptual analysis, you’ll determine the existence of certain concepts within the content and identify their frequency. In other words, conceptual analysis involves the number of times a specific concept appears in the content.

Conceptual analysis is typically focused on explicit data, which means you’ll focus your analysis on a specific concept to identify its presence in the content and determine its frequency.

However, when conducting a content analysis, you can also use implicit data. This approach is more involved, complicated, and requires the use of a dictionary, contextual translation rules, or a combination of both.

No matter what type you use, conceptual analysis brings an element of quantitive analysis into a qualitative approach to research.

Relational content analysis takes conceptual analysis a step further. So, while the process starts in the same way by identifying concepts in content, it doesn’t focus on finding the frequency of these concepts, but rather on the relationships between the concepts, the context in which they appear in the content, and their interrelationships.

Before starting with a relational analysis, you’ll first need to decide on which subcategory of relational analysis you’ll use:

• Affect extraction: With this relational content analysis approach, you’ll evaluate concepts based on their emotional attributes. You’ll typically assess these emotions on a rating scale with higher values assigned to positive emotions and lower values to negative ones. In turn, this allows you to capture the emotions of the writer or speaker at the time the content is created. The main difficulty with this approach is that emotions can differ over time and across populations.
• Proximity analysis: With this approach, you’ll identify concepts as in conceptual analysis, but you’ll evaluate the way in which they occur together in the content. In other words, proximity analysis allows you to analyze the relationship between concepts and derive a concept matrix from which you’ll be able to develop meaning. Proximity analysis is typically used when you want to extract facts from the content rather than contextual, emotional, or cultural factors.
• Cognitive mapping: Finally, cognitive mapping can be used with affect extraction or proximity analysis. It’s a visualization technique that allows you to create a model that represents the overall meaning of content and presents it as a graphic map of the relationships between concepts. As such, it’s also commonly used when analyzing the changes in meanings, definitions, and terms over time.

Now that we’ve seen what content analysis is and looked at the different types of content analysis, it’s important to understand how reliable it is as a research method . We’ll also look at what criteria impact the validity of a content analysis.

There are three criteria that determine the reliability of a content analysis:

• Stability . Stability refers to the tendency of coders to consistently categorize or code the same data in the same way over time.
• Reproducibility . This criterion refers to the tendency of coders to classify categories membership in the same way.
• Accuracy . Accuracy refers to the extent to which the classification of content corresponds to a specific standard.

Keep in mind, though, that because you’ll need to code or categorize the concepts you’ll aim to identify and analyze manually, you’ll never be able to eliminate human error. However, you’ll be able to minimize it.

In turn, three criteria determine the validity of a content analysis:

• Closeness of categories . This is achieved by using multiple classifiers to get an agreed-upon definition for a specific category by using either implicit variables or synonyms. In this way, the category can be broadened to include more relevant data.
• Conclusions . Here, it’s crucial to decide what level of implication will be allowable. In other words, it’s important to consider whether the conclusions are valid based on the data or whether they can be explained using some other phenomena.
• Generalizability of the results of the analysis to a theory . Generalizability comes down to how you determine your categories as mentioned above and how reliable those categories are. In turn, this relies on how accurately the categories are at measuring the concepts or ideas that you’re looking to measure.

Considering everything mentioned above, there are definite advantages and disadvantages when it comes to content analysis:

Let’s now look at the steps you’ll need to follow when doing a content analysis.

The first step will always be to formulate your research questions. This is simply because, without clear and defined research questions, you won’t know what question to answer and, by implication, won’t be able to code your concepts.

Based on your research questions, you’ll then need to decide what content you’ll analyze. Here, you’ll use three factors to find the right content:

• The type of content . Here you’ll need to consider the various types of content you’ll use and their medium like, for example, blog posts, social media, newspapers, or online articles.
• What criteria you’ll use for inclusion . Here you’ll decide what criteria you’ll use to include content. This can, for instance, be the mentioning of a certain event or advertising a specific product.
• Your parameters . Here, you’ll decide what content you’ll include based on specified parameters in terms of date and location.

The next step is to consider your own pre-conception of the questions and identify your biases. This process is referred to as bracketing and allows you to be aware of your biases before you start your research with the result that they’ll be less likely to influence the analysis.

Your next step would be to define the units of meaning that you’ll code. This will, for example, be the number of times a concept appears in the content or the treatment of concept, words, or themes in the content. You’ll then need to define the set of categories you’ll use for coding which can be either objective or more conceptual.

Based on the above, you’ll then organize the units of meaning into your defined categories. Apart from this, your coding scheme will also determine how you’ll analyze the data.

The next step is to code the content. During this process, you’ll work through the content and record the data according to your coding scheme. It’s also here where conceptual and relational analysis starts to deviate in relation to the process you’ll need to follow.

As mentioned earlier, conceptual analysis aims to identify the number of times a specific concept, idea, word, or phrase appears in the content. So, here, you’ll need to decide what level of analysis you’ll implement.

In contrast, with relational analysis, you’ll need to decide what type of relational analysis you’ll use. So, you’ll need to determine whether you’ll use affect extraction, proximity analysis, cognitive mapping, or a combination of these approaches.

Once you’ve coded the data, you’ll be able to analyze it and draw conclusions from the data based on your research questions.

Content analysis offers an inexpensive and flexible way to identify trends and patterns in communication content. In addition, it’s unobtrusive which eliminates many ethical concerns and inaccuracies in research data. However, to be most effective, a content analysis must be planned and used carefully in order to ensure reliability and validity.

The two general types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions.

In qualitative research coding means categorizing concepts, words, and themes within your content to create a basis for analyzing the results. While coding, you work through the content and record the data according to your coding scheme.

Content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. The goal of a content analysis is to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data, depending on the specific use case.

Content analysis is a qualitative method of data analysis and can be used in many different fields. It is particularly popular in the social sciences.

It is possible to do qualitative analysis without coding, but content analysis as a method of qualitative analysis requires coding or categorizing data to then analyze it according to your coding scheme in the next step.

How To Conduct Content Analysis: A Comprehensive Guide

Unlock hidden meanings! Learn how to conduct content analysis, determining the presence of words, themes, or concepts in your data.

Content analysis, a diverse research method, provides an organized approach for dissecting and comprehending communication in its multiple forms. Whether evaluating textual documents, visual images, social media content, or audio recordings, content analysis provides researchers with the tools they need to discover hidden meanings, identify common themes, and expose underlying patterns in varied datasets. 

Through this guide, researchers will understand how to conduct content analysis. This guide aims to serve as a beacon for researchers navigating the complicated landscape of content analysis, providing not only a thorough definition and explanation of its significance, but also practical insights into its application across qualitative and quantitative research paradigms. As methods of communication expand and diversify, knowing and mastering content analysis becomes increasingly important for researchers looking to delve deeper into the complexities of human expression and societal dynamics.

Understanding Content Analysis

As previously stated, content analysis is a robust research process used to evaluate and interpret various types of communication, including text and images, with meticulous attention to detail. 

Before understanding how to conduct content analysis, it’s important to recognize the profound significance this methodology holds in both qualitative and quantitative research paradigms, offering unique advantages and insights to researchers across diverse disciplines.

Related article: Research Paradigm: An Introduction with Examples

Content Analysis On Qualitative Research

• Exploration of Complex Phenomena: Qualitative research seeks to understand both the breadth and depth of human experiences, points of view, and behaviors. Content analysis is a systematic method for analyzing textual, visual, or audio data, allowing researchers to identify complex meanings, patterns, and themes in qualitative information.
• Comprehending Context and Culture: A common goal of qualitative research is to comprehend phenomena with regard to their sociocultural environment. Researchers can study how language, symbols, and representations are created and understood within certain social or cultural contexts by using content analysis.
• Theory Building and Grounded Theory: Grounded theory methods, which allow researchers to construct theories based on empirical data, heavily rely on content analysis. Through methodical examination of qualitative data, researchers can identify emerging themes, enhance theoretical frameworks, and formulate theories based on empirical findings.
• Flexibility and Adaptability: Researchers can customize their approach to the details of their research setting by using content analysis, which provides flexibility in data collecting and analysis. Content analysis can be tailored to accommodate a diverse array of qualitative data sources, including but not limited to interview transcripts, social media posts, and historical documents.

Content Analysis On Quantitative Research

• Standardization and Objectivity: When gathering and analyzing data, quantitative research places a strong emphasis on standardization and objectivity. Textual or visual material can be methodically coded and categorized into quantifiable characteristics by researchers using content analysis, which offers an organized framework for quantifying qualitative data.
• Large-Scale Data Analysis: Content analysis can be scaled up to analyze large volumes of data efficiently. Researchers can examine large datasets and reach statistically significant conclusions by using quantitative content analysis, whether the dataset is online forums, news articles, or survey replies.
• Comparative Analysis and Generalizability: Researchers can find trends, patterns, or discrepancies in content across several contexts by using quantitative content analysis to assist comparative study across texts or historical periods. By quantifying textual data, researchers can also assess the generalizability of findings to broader populations or phenomena.
• Integration with Statistical Methods: To improve data analysis and interpretation, quantitative content analysis can be combined with statistical techniques. Techniques such as frequency counts, chi-square tests, or regression analysis can be applied to analyze coded content and test hypotheses derived from theoretical frameworks.

Types Of Content Analysis

• Manifest Content Analysis: Manifest content analysis focuses on analyzing the explicit, surface-level content of communication. It involves identifying and categorizing visible, tangible elements such as words, phrases, or images within the text or other forms of media. The goal is to describe and quantify the visible characteristics of communication without delving into deeper meanings or interpretations.
• Latent Content Analysis: Latent content analysis goes beyond the explicit content to uncover underlying meanings, themes, and interpretations embedded within the communication. It involves interpreting the implicit, hidden messages, symbols, or metaphors conveyed through language, imagery, or other forms of representation. The aim is to uncover deeper insights into the underlying motives, beliefs, or attitudes reflected in the communication.
• Thematic Analysis: Thematic analysis involves identifying, analyzing, and interpreting recurring themes or patterns within the content. It focuses on discovering commonalities, differences, and relationships between concepts or ideas expressed within the communication. The goal is to uncover overarching themes or conceptual categories that capture the essence of the data and provide insights into the underlying phenomena being studied.
• Narrative Analysis: Narrative analysis focuses on analyzing the structure, content, and meaning of narratives or stories within the communication. It involves examining the plot, characters, settings, and other narrative elements to uncover the underlying themes, ideologies, or cultural meanings embedded within the stories. The aim is to understand how narratives shape identity, culture, and social discourse.
• Discourse Analysis: Discourse analysis examines the language, rhetoric, and power dynamics inherent in communication practices. It involves analyzing how language is used to construct social realities, shape identities, and negotiate power relations within specific contexts. The goal is to uncover how language structures and reflects social norms, ideologies, and power dynamics within society.
• Visual Content Analysis: Visual content analysis focuses on analyzing visual elements such as images, symbols, or graphics within communication media. It involves examining the composition, content, and meaning of visual representations to uncover underlying themes, messages, or cultural meanings conveyed through imagery. The aim is to understand how visuals influence perception, cognition, and communication processes.

Preparing For Content Analysis

Before embarking on the journey of content analysis, researchers must lay a solid groundwork by carefully selecting materials for analysis and defining clear categories for coding. This preparatory phase is crucial for ensuring the relevance, reliability, and validity of the content analysis process.

Material Selection

Criteria for choosing materials.

• Relevance to Research Objectives: Select materials that are directly relevant to the research questions or objectives. Ensure that the content aligns with the scope and focus of the study.
• Diversity and Representation: Choose materials that provide a diverse range of perspectives, viewpoints, or contexts relevant to the research topic. Seek to include a variety of sources to capture different dimensions of the phenomenon under study.
• Accessibility and Availability: Prioritize materials that are readily accessible and available for analysis. Consider factors such as copyright restrictions, data availability, and ethical considerations when selecting materials.
• Quality and Authenticity: Verify the credibility and authenticity of the materials to ensure the accuracy and reliability of the data. Use reputable sources and validate the authenticity of primary data sources where applicable.

How To Acquire Materials

• Literature Review: Conduct a comprehensive literature review to identify relevant sources, studies, or datasets related to the research topic. Utilize academic databases, libraries, and online repositories to access scholarly articles, books, reports, and other relevant materials.

Also read: What is a literature review? Get the concept and start using it

• Data Collection: Collect primary data through methods such as interviews, surveys, observations, or document analysis, depending on the research design. Use systematic sampling techniques to ensure representativeness and diversity in the selection of materials.
• Digital Sources: Explore digital sources such as online databases, social media platforms, websites, or multimedia archives to access digital content for analysis. Use web scraping tools, APIs, or data extraction techniques to gather digital data in a structured format.
• Ethical Considerations: Adhere to ethical guidelines and obtain necessary permissions or approvals for accessing and using copyrighted materials or sensitive data. Protect the privacy and confidentiality of participants and respect intellectual property rights when acquiring materials for analysis.

Defining And Identifying Categories

How to define categories.

• Define Research Objectives: Clarify the research questions, objectives, and hypotheses to guide the development of coding categories. Determine the key concepts, themes, or variables of interest that will be coded and analyzed.
• Conduct Preliminary Analysis: Review the selected materials to identify recurring patterns, themes, or topics relevant to the research focus. Use open coding techniques to generate initial categories based on the content of the materials.
• Conceptualize Categories: Organize the initial codes into conceptual categories or thematic domains that encapsulate the main dimensions of the phenomenon under study. Group related codes together and refine the category labels to ensure clarity and coherence.
• Establish Coding Rules: Develop clear and concise coding rules or definitions for each category to guide the coding process. Define inclusion and exclusion criteria, coding criteria, and examples to illustrate the application of each category.
• Pilot Test Categories: Conduct a pilot test or inter-coder reliability assessment to evaluate the clarity, reliability, and validity of the coding categories. Revise and refine the categories based on feedback from pilot testing to improve coding consistency and accuracy.

Best Practices To Identify Categories

• Iterative Process: Approach category development as an iterative process, refining and revising categories based on ongoing analysis and feedback. Continuously review and update categories to capture emerging themes or insights.
• Triangulation: Use multiple sources of data or multiple coders to triangulate findings and ensure the reliability and validity of coding categories. Compare and cross-reference coding results to identify discrepancies or inconsistencies.
• Peer Review: Seek feedback from colleagues, mentors, or experts in the field to validate the relevance and appropriateness of coding categories. Engage in peer review sessions to discuss and refine coding schemes collaboratively.
• Reflexivity: Maintain reflexivity throughout the category development process, critically reflecting on your assumptions, biases, and interpretations. Consider alternative perspectives and interpretations to enhance the richness and depth of coding categories.
• Consult Existing Frameworks: Draw upon existing theoretical frameworks, conceptual models, or coding schemes relevant to the research topic. Adapt and modify existing frameworks to suit the specific context and objectives of the study.

How To Conduct Content Analysis

Mastering content analysis empowers researchers to uncover insights and contribute to scholarly discourse across various disciplines. By following the guidelines outlined in this guide, researchers can conduct meaningful analyses that advance knowledge and inform decision-making processes.

Coding Content

To create an effective coding system, start by identifying the key concepts, themes, or variables you want to analyze within your content. Develop clear and concise code definitions and coding rules to guide the coding process. Ensure that your coding system is comprehensive, covering all relevant aspects of the content you are analyzing. Once your coding system is in place, apply it consistently and systematically to the entire dataset.

Let’s say you’re conducting a content analysis on customer reviews of a product. Your coding system may include categories such as “product quality,” “customer service,” and “value for money.” As you analyze each review, you’ll assign codes to relevant segments of text based on these categories. For example, a positive comment about the product’s durability may be coded under “product quality,” while a complaint about slow shipping may be coded under “customer service.”

Analyzing And Interpreting Results

Once you’ve coded your content, you can begin analyzing it to identify patterns, trends, and insights. Common techniques for analyzing content include frequency analysis, thematic analysis, and comparative analysis. Use these techniques to uncover key themes, relationships between variables, and variations across different segments of your dataset.

When interpreting your content analysis results, consider the context in which the content was produced and the characteristics of your sample. Look for overarching patterns and trends, but also pay attention to outliers or unexpected findings. Consider how your findings relate to existing literature and theories in your field, and be transparent about any limitations or biases in your analysis.

Validating The Results

Validating results in content analysis involves assessing the reliability and validity of your findings to ensure they accurately reflect the underlying content. This may include measures to ensure inter-coder reliability, triangulation with other data sources, and sensitivity analyses to test the robustness of your results.

Common methods used to validate results in content analysis include inter-coder reliability tests, where multiple coders independently code a subset of the data to assess consistency. Triangulation involves comparing findings from content analysis with other methods or sources of data to confirm or refute conclusions. Additionally, sensitivity analyses involve testing the impact of different coding decisions or analytical approaches on the results to assess their robustness.

Reporting Findings

In reporting findings, researchers distill the essence of their content analysis, presenting insights and conclusions clearly and concisely. This section is a very important part of how to conduct content analysis, as it provides guidance on structuring reports, writing effectively, and using visual aids to convey results with clarity and impact.

Writing And Structuring The Report

When writing your content analysis report, start by clearly stating your research objectives and methodology. Present your findings in a logical and organized manner, using descriptive statistics, tables, and visual aids to support your analysis. Discuss the implications of your findings for theory, practice, or policy, and conclude by summarizing the key insights and contributions of your study.

An effective content analysis report should be concise, clear, and well-structured. Use headings and subheadings to guide the reader through the report, and provide sufficient detail to support your conclusions. Be transparent about your methods and any limitations of your analysis, and use language that is accessible to your intended audience.

Organize your report into sections that mirror the steps of your content analysis process, such as coding, analysis, and interpretation. Use descriptive titles and subheadings to clearly delineate each section, and provide ample context and explanation for your findings. Consider including visual aids such as charts or graphs to enhance the clarity and readability of your report.

Visualising Data

Visualizing data is an effective way to communicate your findings and insights to your audience. Common visualizations used in content analysis include bar charts, pie charts, line graphs, and heat maps. Choose the visualization method that best represents the patterns and trends in your data and is most suitable for your audience.

Consider the nature of your data and the preferences of your audience when selecting visualization methods. For example, bar charts are useful for comparing frequencies or proportions across categories, while line graphs are suitable for showing trends over time. Choose visualization methods that are intuitive, informative, and visually appealing to effectively convey your content analysis results.

Related article: Art Of Describing Graphs And Representing Numbers Visually

Tips For A Successful Content Analysis

• Document Your Process: Keeping detailed records of your content analysis process can prove invaluable, aiding in transparency, reproducibility, and troubleshooting. Record decisions made during material selection, category definition, and coding, as well as any challenges encountered and their resolutions. This documentation not only enhances the rigor of your analysis but also facilitates communication with collaborators and reviewers.
• Embrace Iteration: Content analysis is rarely a linear process. Embrace iteration and refinement throughout each stage, from material selection to reporting findings. Regularly revisit and revise coding categories, analytical techniques, and interpretations in response to emerging insights or challenges. Iterative refinement ensures that your analysis remains dynamic and responsive to the complexities of the data.
• Utilize Software Tools: While content analysis can be conducted manually, leveraging software tools can streamline and enhance the process. Explore software options tailored to content analysis tasks, such as qualitative data analysis software (QDAS) or text analysis tools. These tools often offer features for organizing data, coding text, and visualizing results, saving time and enhancing analytical capabilities.
• Prioritize Inter-Coder Reliability: Inter-coder reliability, or the consistency of coding among multiple coders, is crucial for ensuring the validity and reliability of your analysis. Prioritize inter-coder reliability assessments early in the process, involving multiple coders in coding tasks and comparing their results. Establishing clear coding guidelines and conducting regular reliability checks can mitigate discrepancies and enhance the credibility of your findings.
• Consider Cultural Sensitivity: When analyzing content that reflects cultural or linguistic diversity, it’s essential to approach the process with sensitivity and awareness. Consider the cultural context of the content, including language nuances, symbolism, and cultural norms, when interpreting and coding data. Engage with diverse perspectives and seek input from stakeholders to ensure that your analysis accurately reflects the complexity of the cultural landscape.
• Be Mindful of Bias: Conscious and unconscious biases can influence every stage of the content analysis process, from material selection to interpretation of results. Stay vigilant for biases related to personal beliefs, disciplinary perspectives, or preconceived notions about the topic under study. Implement strategies to mitigate bias, such as peer review, reflexivity exercises, and triangulation with multiple data sources.
• Foster Collaboration: Content analysis can benefit from interdisciplinary collaboration and diverse perspectives. Engage with colleagues, mentors, or experts from different fields to enrich your analysis and challenge assumptions. Collaborative approaches can foster creativity, rigor, and innovation, leading to more robust and nuanced findings.
• Stay Open to Serendipity: While content analysis often involves systematic data collection and analysis, don’t overlook the potential for serendipitous discoveries. Remain open to unexpected insights, patterns, or connections that emerge during the analysis process. Serendipity can lead to novel research directions, enriching your understanding of the phenomenon under study.

Science Figures, Graphical Abstracts, And Infographics For Your Research

Mind the Graph is a valuable resource for scientists seeking to enhance the visual communication of their research through science figures, graphical abstracts, and infographics. With its user-friendly interface, extensive template library, and customizable design tools, the platform empowers researchers to create visually compelling and scientifically accurate visualizations that effectively communicate complex ideas and findings to a diverse audience.

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The Content Analysis Guidebook

• Edition: Second
• By: Kimberly A. Neuendorf
• Publisher: SAGE Publications, Inc
• Publication year: 2017
• Online pub date: December 20, 2019
• Discipline: Communication and Media Studies
• Methods: Content analysis , Measurement , Coding
• DOI: https:// doi. org/10.4135/9781071802878
• Keywords: coding schemes , dictionaries , films , interactive media , population , social media , web sites Show all Show less
• Print ISBN: 9781412979474
• Online ISBN: 9781071802878
• Buy the book icon link

Subject index

Content analysis is one of the most important but complex research methodologies in the social sciences. In this thoroughly updated Second Edition of The Content Analysis Guidebook, author Kimberly Neuendorf draws on examples from across numerous disciplines to clarify the complicated aspects of content analysis through step-by-step instruction and practical advice. Throughout the book, the author also describes a wide range of innovative content analysis projects from both academia and commercial research that provide readers with a deeper understanding of the research process and its many real-world applications.

Front Matter

• List of Boxes
• List of Tables and Figures
• Acknowledgments
• Chapter 1 | Defining Content Analysis
• Chapter 2 | An Integrative Approach to Content Analysis
• Chapter 3 | Message Units and Sampling
• Chapter 4 | Variables and Predictions
• Chapter 5 | Measurement and Validity
• Chapter 6 | Reliability
• Chapter 7 | Content Analysis in the Interactive Media Age
• Chapter 8 | Results and Reporting
• Chapter 9 | Contexts

Back Matter

• Resource 1: CATA—Computer-Aided Text Analysis Options
• Resource 2: The Content Analysis Guidebook Online (CAGO)
• Author Index
• About the Authors

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Web Content Analysis: Expanding the Paradigm

• First Online: 01 January 2010

Cite this chapter

• Susan C. Herring 4  

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Are established methods of content analysis (CA) adequate to analyze web content, or should new methods be devised to address new technological developments? This article addresses this question by contrasting narrow and broad interpretations of the concept of web content analysis. The utility of a broad interpretation that subsumes the narrow one is then illustrated with reference to research on weblogs (blogs), a popular web format in which features of HTML documents and interactive computer-mediated communication converge. The article concludes by proposing an expanded Web Content Analysis (WebCA) paradigm in which insights from paradigms such as discourse analysis and social network analysis are operationalized and implemented within a general content analytic framework.

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Content Analysis Technique

Texts as Data IV: Web Crawling, Content and Link Analyses

Automated Content Analysis

While McMillan ( 2000 ) acknowledges that “the size of the sample depends on factors such as the goals of the study” (p. 2, emphasis added), she does not mention that different research goals/questions might call for different types of samples. Rather, she asserts that random samples are required for “rigor” in all CA studies—a claim that many researchers would dispute (see, e.g., note 5).

For descriptions of these and other classic interrater reliability measures, see Scott ( 1955 ), Holsti ( 1969 ), and Krippendorff ( 1980 , 2008).

In a review of 25 years of content analyses, Riffe and Freitag (1997; cited in Weare & Lin, 2000 ) found that most studies were based on convenience or purposive samples; only 22.2% of the studies attempted to be representative of the population of interest.

On grounded theory, see Glaser and Strauss ( 1967 ).

Herring ( 2004 , p. 350) notes that “in CMDA, [sampling] is rarely done randomly, since random sampling sacrifices context, and context is important in interpreting discourse analysis results.”

This estimate is based on a report that the number of blogs created at major hosts was 134-144 million in October 2005 ( http://www.blogherald.com/2005/10/10/the-blog-herald-blog-count-october-2005 /, accessed December 7, 2007). Blog creation, especially in countries outside the U.S., has increased since then, although many blogs have also been abandoned (Wikipedia, June 28, 2008 ).

The (We)blog Research on Genre (BROG) project. See http://en.wikipedia.org/wiki/BROG , accessed August 26, 2009 .

For example, Herring, Scheidt, et al. (2004, 2005) found that contrary to popular claims that blog entries typically contain links and link often to other blogs, the average number of links in entries in randomly-selected blogs was .65, and most entries contained 0 links. Moreover, the majority of links were to websites created by others, with links to other blogs coming in a distant third.

See, e.g., Herring, Scheidt, et al. (2004, 2005); Mishne and Glance ( 2006 ).

This study is an exception to the generalization that most computational web studies do not orient toward content analysis. The stated goal of Nakajima et al. ( 2005 , p. 1) is to capture and analyze “conversational web content” in blogs.

Ali-Hasan, N., & Adamic, L. (2007). Expressing social relationships on the blog through links and comments . Paper presented at the international conference for weblogs and social media, Boulder, CO.

Google Scholar  

Balog, K., Mishne, G., & Rijke, M. (2006). Why are they excited? Identifying and explaining spikes in blog mood levels. Paper presented at the 11th meeting of the European Chapter of the Association for Computational Linguistics, Trento, Italy.

Baran, S. J. (2002). Introduction to mass communication (2nd ed.) New York: McGraw-Hill.

Bates, M. J., & Lu, S. (1997). An exploratory profile of personal home pages: Content, design, metaphors. Online and CDROM Review, 21 (6), 331–340.

Article   Google Scholar  

Bauer, M. (2000). Classical content analysis: A review. In M. W. Bauer & G. Gaskell (Eds.), Qualitative researching with text, image, and sound: A practical handbook (pp. 131–151). London: Sage.

Berelson, B. (1952). Content analysis in communication research . New York: Free Press.

Berelson, B., & Lazarsfeld, P. F. (1948). The analysis of communication content . Chicago/New York: University of Chicago and Columbia University.

Blood, R. (2002). Introduction. In J. Rodzvilla (Ed.), We’ve got blog: How weblogs are changing our culture (pp. ix–xiii). Cambridge, MA: Perseus.

Bush, C. R. (1951). The analysis of political campaign news. Journalism Quarterly, 28 (2), 250–252.

Dimitrova, D. V., & Neznanski, M. (2006). Online journalism and the war in cyberspace: A comparison between U.S. and international newspapers. Journal of Computer-Mediated Communication, 12 (1), Article 13. Retrieved from http://jcmc.indiana.edu/vol12/issue1/dimitrova.html

Efimova, L., & de Moor, A. (2005). Beyond personal web publishing: An exploratory study of conversational blogging practices. Proceedings of the Thirty-Eighth Hawaii International Conference on System Sciences . Los Alamitos, CA: IEEE.

Fogg, B. J., Kameda, T., Boyd, J., Marshall, J., Sethi, R., Sockol, M., et al. (2002). Stanford-Makovsky web credibility study 2002: Investigating what makes web sites credible today. Retrieved from http://captology.stanford.edu/pdf/Stanford-MakovskyWebCredStudy2002-prelim.pdf

Foot, K. A., Schneider, S. M., Dougherty, M., Xenos, M., & Larsen, E. (2003). Analyzing linking practices: Candidate sites in the 2002 U.S. electoral Web sphere. Journal of Computer-Mediated Communication, 8 (4). Retrieved from http://jcmc.indiana.edu/vol8/issue4/foot.html

Gibson, G., Kleinberg, J., & Raghavan, P. (1998). Inferring web communities from link topology. Proceedings of the 9th ACM Conference on Hypertext and Hypermedia. Pittsburgh, PA: ACM.

Glaser, B., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.

Herring, S. C. (2004). Computer-mediated discourse analysis: An approach to researching online behavior. In S. A. Barab, R. Kling, & J. H. Gray (Eds.), Designing for virtual communities in the service of learning (pp. 338–376). New York: Cambridge University Press.

Herring, S. C., & Paolillo, J. C. (2006). Gender and genre variation in weblogs. Journal of Sociolinguistics, 10 (4), 439–459.

Herring, S. C., Kouper, I., Paolillo, J., Scheidt, L. A., Tyworth, M., Welsch, P., et al. (2005). Conversations in the blogosphere: An analysis “from the bottom up.” Proceedings of the Thirty-Eighth Hawai’i International Conference on System Sciences . Los Alamitos, CA: IEEE.

Herring, S. C., Scheidt, L. A., Bonus, S., & Wright, E. (2004). Bridging the gap: A genre analysis of weblogs. Proceedings of the Thirty-Seventh Hawai’i International Conference on System Sciences . Los Alamitos, CA: IEEE.

Herring, S. C., Scheidt, L. A., Bonus, S., & Wright, E. (2005). Weblogs as a bridging genre. Information, Technology & People, 18 (2), 142–171.

Herring, S. C., Scheidt, L. A., Kouper, I., & Wright, E. (2006). Longitudinal content analysis of weblogs: 2003–2004. In M. Tremayne (Ed.), Blogging, citizenship, and the future of media (pp. 3–20). London: Routledge.

Holsti, O. R. (1969). Content analysis for the social sciences and humanities . Reading, MA: Addison Wesley.

Huffaker, D. A., & Calvert, S. L. (2005). Gender, identity and language use in teenage blogs. Journal of Computer-Mediated Communication, 10 (2). Retrieved from http://jcmc.indiana.edu/vol10/issue2/huffaker.html

Jackson, M. (1997). Assessing the structure of communication on the world wide web. Journal of Computer-Mediated Communication, 3 (1). Retrieved from http://www.ascusc.org/jcmc/vol3/issue1/jackson.html

Krippendorff, K. (1980). Content analysis: An introduction to its methodology . Newbury Park: Sage.

Krippendorff, K. (2008). Testing the reliability of content analysis data: What is involved and why. In K. Krippendorff & M. A. Bock (Eds.), The content analysis reader (pp. 350–357). Thousand Oaks, CA: Sage. Retrieved from http://www.asc.upenn.edu/usr/krippendorff/dogs.html

Kutz, D. O., & Herring, S. C. (2005). Micro-longitudinal analysis of web news updates. Proceedings of the Thirty-Eighth Hawai’i International Conference on System Sciences . Los Alamitos, CA: IEEE.

McMillan, S. J. (2000). The microscope and the moving target: The challenge of applying content analysis to the world wide web. Journalism and Mass Communication Quarterly, 77 (1), 80–98.

MathSciNet   Google Scholar  

Mishne, G., & Glance, N. (2006). Leave a reply: An analysis of weblog comments . Proceedings of the 3rd Annual Workshop on the Weblogging Ecosystem, 15th World Wide Web Conference, Edinburgh.

Mitra, A. (1999). Characteristics of the WWW text: Tracing discursive strategies. Journal of Computer-Mediated Communication, 5 (1). Retrieved from http://www.ascusc.org/jcmc/vol5/issue1/mitra.html

Mitra, A., & Cohen, E. (1999). Analyzing the web: Directions and challenges. In S. Jones (Ed.), Doing internet research: Critical issues and methods for examining the net (pp. 179–202). Thousand Oaks, CA: Sage.

Nakajima, S., Tatemura, J., Hino, Y., Hara, Y., & Tanaka, K. (2005). Discovering important bloggers based on analyzing blog threads. Paper presented at WWW2005, Chiba, Japan.

Park, H. W. (2003). What is hyperlink network analysis? New method for the study of social structure on the web. Connections, 25 (1), 49–61.

Pfeil, U., Zaphiris, P., & Ang, C. S. (2006). Cultural differences in collaborative authoring of Wikipedia. Journal of Computer-Mediated Communication, 12 (1), Article 5. Retrieved from http://jcmc.indiana.edu/vol12/issue1/pfeil.html

Scheidt, L. A., & Wright, E. (2004). Common visual design elements of weblogs. In L. Gurak, S. Antonijevic, L. Johnson, C. Ratliff, & J. Reyman (Eds.), Into the blogosphere: Rhetoric, community, and culture of weblogs. Retrieved from http://blog.lib.umn.edu/blogosphere /

Schneider, S. M., & Foot, K. A. (2004). The web as an object of study. New Media & Society, 6 (1), 114–122.

Scott, W. (1955). Reliability of content analysis: The case of nominal scale coding. Public Opinion Quarterly, 17 , 321–325.

Singh, N., & Baack, D. W. (2004). Web site adaptation: A cross-cultural comparison of U.S. and Mexican web sites. Journal of Computer-Mediated Communication, 9 (4). Retrieved from http://jcmc.indiana.edu/vol9/issue4/singh_baack.html

Thelwall, M. (2002). The top 100 linked pages on UK university web sites: High inlink counts are not usually directly associated with quality scholarly content. Journal of Information Science, 28 (6), 485–493.

Trammell, K. D. (2006). Blog offensive: An exploratory analysis of attacks published on campaign blog posts from a political public relations perspective. Public Relations Review, 32 (4), 402–406.

Trammell, K. D., Tarkowski, A., Hofmokl, J., & Sapp, A. M. (2006). Rzeczpospolita blogów [Republic of Blog]: Examining Polish bloggers through content analysis. Journal of Computer-Mediated Communication, 11 (3), Article 2. Retrieved from http://jcmc.indiana.edu/vol11/issue3/trammell.html

Tremayne, M., Zheng, N., Lee, J. K., & Jeong, J. (2006). Issue publics on the web: Applying network theory to the war blogosphere. Journal of Computer-Mediated Communication, 12 (1), Article 15. Retrieved from http://jcmc.indiana.edu/vol12/issue1/tremayne.html

Wakeford, N. (2000). New media, new methodologies: Studying the web. In D. Gauntlett (Ed.), Web.studies: Rewiring media studies for the digital age (pp. 31–42). London: Arnold.

Waseleski, C. (2006). Gender and the use of exclamation points in computer-mediated communication: An Analysis of exclamations posted to two electronic discussion lists. Journal of Computer-Mediated Communication, 11 (4), Article 6. Retrieved http://jcmc.indiana.edu/vol11/issue4/waseleski.html

Weare, C., & Lin, W. Y. (2000). Content analysis of the world wide web – Opportunities and challenges. Social Science Computer Review, 18 (3), 272–292.

Wikipedia. (2008). Blog . Retrieved on June 28, 2008, from http://en.wikipedia.org/wiki/Blog

Williams, P., Tramell, K., Postelnicu, M., Landreville, K., & Martin, J. (2005). Blogging and hyperlinking: Use of the web to enhance visibility during the 2004 U.S. campaign. Journalism Studies, 6 (2), 177–186.

Young, J., & Foot, K. (2005). Corporate e-cruiting: The construction of work in Fortune 500 recruiting web sites. Journal of Computer-Mediated Communication, 11 (1), Article 3. Retrieved from http://jcmc.indiana.edu/vol11/issue1/young.html

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Herring, S.C. (2009). Web Content Analysis: Expanding the Paradigm. In: Hunsinger, J., Klastrup, L., Allen, M. (eds) International Handbook of Internet Research. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9789-8_14

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