Word Association Thematic Analysis: A Social Media Text Exploration Strategy (Synthesis Lectures on Information Concepts, Retrieval, and Services)

Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, thenidentifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages. This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts. Acknowledgments Introduction 1.1 Overview: Word Association Detection, Contextualization and Thematic Analysis 1.2 WAA and WATA Examples 1.3 Research Philosophy: Mixed Methods 1.4 Comparison with Traditional Social Research Methods 1.5 Software: Mozdeh 1.6 Language and International Considerations 1.7 Using this Book Data Collection with Mozdeh 2.1 Sample Size 2.2 Data Collection Methods 2.2.1 Query-Based Post Collection (Twitter) 2.2.2 User-Based Post Collection (Twitter, YouTube) 2.2.3 User Profile Bio Collection (Twitter) 2.2.4 Other Methods to Identify a Set of Texts for Analysis 2.3 Mozdeh Data Collection Procedures 2.3.1 Query-Based Post Collection in Mozdeh 2.3.2 User-Based Post Collection with Mozdeh 2.3.3 User Profile Bio Collection with Mozdeh 2.3.4 Importing Text Collections (Academic Publications, Other) into Mozdeh 2.4 Filtering the Initial Dataset 2.5 Summary Word Association Detection: Term Identification 3.1 Word Association Detection: Subset vs. the Rest 3.1.1 Choice of Comparator Set 3.1.2 Word Association Detection with Mozdeh 3.2 Word Association Detection: Subset A vs. Subset B 3.3 Statistical Details 3.3.1 Theoretical Assumptions 3.3.2 Effect Sizes 3.4 Protection Against Accidentally Finding Irrelevant Words 3.5 Sample Size Requirements 3.6 Limitations 3.7 Summary Word Association Contextualization: Term Meaning and Context 4.1 Word Association Contextualization from a Random Sample of Relevant Texts 4.1.1 Word Association Contextualization Overview 4.1.2 Creating the WAC Text Sample with Mozdeh 4.1.3 Detecting Typical Meanings for Polysemous Words and Words in Expressions 4.1.4 Identifying Word Contexts 4.1.5 Difficult Cases: Pronouns and Stylistic Words 4.1.6 Random Sample Summary 4.2 Advanced Word Association Contextualization 4.2.1 Follow-Up Word Association Detection for the Term 4.2.2 Follow-Up Word Association Detection Within Texts Containing the Term 4.3 Word Association Contextualization Test Assumptions 4.4 Causes of Word Associations: News, Viral Sharing, or Societal Differences? 4.5 Using Multiple Coders 4.6 Reporting WAA 4.7 Summary Word Association Thematic Analysis: Theme Detection 5.1 Assigning Themes to Words 5.2 WATA Procedure 5.3 WATA Visual and Performing Arts Example 5.4 Using Multiple Coders 5.5 Reporting WATA 5.5.1 Theme Prevalence Information 5.5.2 Discussing the Results 5.6 Summary Word Association Thematic Analysis Examples 6.1 Gender Differences in Early Covid-19 Wweeting 6.2 Gender Differences in Museum Comments on YouTube 6.3 Gender Differences in AcademicPublishing 6.4 International Differences in Academic Nursing Publishing 6.5 Summary Comparison Between WATA and Other Methods 7.1 Content Analysis 7.2 Thematic Analysis 7.3 Word Clouds 7.4 Top Hashtags 7.5 Document Clustering 7.6 Document Set Mapping 7.7 Topic Modeling 7.8 Text/Semantic Networks 7.9 Natural Language Pprocessing and Pre-Processing Possibilities 7.10 Corpus-Assisted Critical Discourse Analysis 7.11 Summary Ethics 8.1 Is Permission Required to Analyze public texts? 8.2 Can Posters Be Named or Quoted? 8.3 Summary Project Planning Summary References Author Biography Blank Page Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, then identifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages. This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts 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 "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 provides an accessible core text for upper-level undergraduates and graduate students across the social sciences. Comprising step-by-step instructions and practical advice, this text unravels the complicated aspects of content analysis."-- Provided by publisher