Identifying Diversity of Thought on Social Media
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Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2019 Identifying Diversity of Thought on Social Media Beth Bullemer Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Industrial and Organizational Psychology Commons Repository Citation Bullemer, Beth, "Identifying Diversity of Thought on Social Media" (2019). Browse all Theses and Dissertations. 2154. https://corescholar.libraries.wright.edu/etd_all/2154 This Dissertation is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. IDENTIFYING DIVERSITY OF THOUGHT ON SOCIAL MEDIA A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by BETH BULLEMER B.F.A., Carnegie Mellon University, 2004 M.S., Wright State University, 2015 2019 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL April 23, 2019 I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Beth Bullemer ENTITLED Identifying Diversity of Thought on Social Media BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. Valerie L. Shalin, Ph.D. Dissertation Director Scott N. J. Watamaniuk, Ph.D. Graduate Program Director Debra Steele-Johnson, Ph.D. Chair, Department of Psychology Barry Milligan, Ph.D. Interim Dean of the Graduate School Committee on Final Examination: Gary Burns, Ph.D. Joe Houpt, Ph.D. Valerie L. Shalin, Ph.D. Amit Sheth, Ph.D. ABSTRACT Bullemer, Beth. Ph.D., Department of Psychology, Wright State University, 2019. Identifying Diversity of Thought on Social Media. This dissertation considers what it means to think differently, using naturalistic verbal evidence. This problem is inspired by a gap within the Wisdom of the Crowd (WoC) literature, but relevant to the study of language processes, mental models, and the vast emerging resource of social media data. I propose a methodological framework to characterize diversity of thought through the quantification of social media data. Four stages of research considered: a) the properties of a sample domain, b) how to identify and select diagnostic content using classification methods, c) how to quantify qualitative content in order to categorize and compare individuals, and d) how to assess the relative merits and challenges of content classification methods, including whether differences in thought actually affect outcomes. The emphasis is on pervasive issues pertinent the analysis of unstructured verbal data, rather than the specific, albeit largely successful solutions explored. Such issues were identified when defining and applying the methodological framework, and generally indicate the influence of sample domain on process measures, success at higher levels of abstraction, and a lack of continuity between all levels of analysis. iii TABLE OF CONTENTS Page 1. INTRODUCTION .......................................................................................................... 1 1.1 Research Objectives .......................................................................................... 1 1.2 Wisdom of the Crowd and Diversity ................................................................ 2 1.3 Characterization of Thought ............................................................................. 4 1.4 Language as an Input ........................................................................................ 7 2. OVERVIEW OF METHODOLOGICAL APPROACH .............................................. 13 3. SAMPLE DOMAIN ..................................................................................................... 18 3.1 Reasoning Rules and Constraints .................................................................... 19 3.2 The Decision of Captain Selection ................................................................. 21 3.3 Available Data ................................................................................................ 22 4. FRAMEWORKS .......................................................................................................... 32 4.1 Framework Definition ..................................................................................... 33 4.2 Clancey’s Problem Types - A Domain-General Framework .......................... 36 4.3 Searle’s Illocutionary Speech Acts - A Domain-General Framework ............ 41 4.4 Influence of Popularity - A Domain-Specific Framework ............................. 47 4.5 Weekly Focus - A Domain- Specific Framework ........................................... 51 4.6 Implications of Decisions associated with Framework Definition ................. 53 iv 5. DIAGNOSTIC FEATURES ......................................................................................... 58 5.1 LEXICAL ITEM ANALYSIS ........................................................................ 61 5.2 Abstracted Word Feature Analysis ................................................................. 66 5.3 Themes Observed with both Language Analysis Techniques ........................ 69 6. CONTENT QUANTIFICATION AND CATEGORIZATION OF INDIVIDUALS .. 74 6.1 Z-Scores .......................................................................................................... 75 6.2 Discrete Categorization Scheme ..................................................................... 80 7. RELATIONSHIPS BETWEEN CLASSIFICATION METHODS .............................. 88 7.1 Series of Simple Effects-Style Correlations ................................................... 89 7.2 Relationships between Language Analysis Techniques ................................. 90 7.3 Relationships between Strategies .................................................................... 91 8. RELATIONSHIPS BETWEEN CLASSIFICATION METHODS AND PERFORMANCE – INDIVIDUAL LEVEL .................................................................. 101 8.1 Series of One-Way ANOVAs ....................................................................... 102 8.2 Relationships between Classification Methods and Performance Outcomes 102 9. RELATIONSHIPS BETWEEN CLASSIFICATION METHODS AND PERFORMANCE – GROUP LEVEL ............................................................................ 110 9.1 Series of t-tests .............................................................................................. 111 9.2 Relationships between Classification Methods and Captain Selection Scores ............................................................................................................................. 113 10. CONCLUSION ......................................................................................................... 126 10.1 Summary of Issues ...................................................................................... 126 v 10.2 Domain Influence ........................................................................................ 131 10.3 Levels of Abstraction .................................................................................. 134 11. CONTRIBUTIONS .................................................................................................. 141 11.1 Contributions relative to Research Objectives ............................................ 141 11.2 Opportunities ............................................................................................... 145 11.3 Summary ..................................................................................................... 146 APPENDICES ................................................................................................................ 148 REFERENCES ............................................................................................................... 324 vi LIST OF FIGURES Figure ............................................................................................................................. Page 1. Overview of methodological approach with section annotations ................................. 14 2. Overview of methodological approach with issue annotations .................................... 17 3. Overlaps in FPL data .................................................................................................... 23 4. Samples of tweets from FPL manager population ........................................................ 25 5. Transformation of captain selection into performance score ........................................ 26 6. Distribution of captain selection scores ........................................................................ 27 7. Distribution of expertise scores .................................................................................... 30 8. Relationship between captain selection scores and expertise scores ............................ 31 9. Clancey’s hierarchy of system operations .................................................................... 37 10. Discrete categorization scheme ................................................................................... 82 11. Distributions of FPL managers ................................................................................... 85 12. Interclass and interclass relationships.. ....................................................................... 99 13. Differences in the