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Psychological Meme Science by Ian Dennis Miller a Thesis Submitted In Psychological Meme Science by Ian Dennis Miller A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Psychology University of Toronto c Copyright 2019 by Ian Dennis Miller Abstract Psychological Meme Science Ian Dennis Miller Doctor of Philosophy Graduate Department of Psychology University of Toronto 2019 Memes are ideas, often represented using media, with the special characteristics of being repeatable and adaptable. Memes impact our lives in material ways, influencing political systems and propagating the stories our shared culture is built from. When propagated via online social networks, the massive scale at which memes operate is without precedent. However, the meme does not act on its own; it is only by human activity that memes are created and proliferated. This dissertation will tackle a series of research questions surrounding the scientific study of humans and memes from a psychological perspective. This work begins with the observation that science is a social enterprise and scientific ideas spread as memes. The first chapter of this dissertation applies social network methods to the global scientific collaboration network in order to build a map of beliefs about systems of humans and memes. The next chapter examines a hierarchical democratic phenomenon - the online campaign preceding an election - in order to determine the appropriate analytical scope for investigating complex systems of political memes. The final chapter presents a method for translating regression models from the psychological literature into computational social simulations using agent-based models. A computational social simulation of urban legends is then built, replicating a study from the literature and then extending it to examine the effect of social network topology upon the propagation of urban legends. Humans and memes, together, constitute a complex system that offers new methodological tools to study the human condition. ii Acknowledgements First and foremost, it is my pleasure to acknowledge the contributions of my advisor, Prof. Gerald Cupchik, who made this possible in the first place. The other members of my doctoral committee, Prof. Doug Bors and Prof. Jacob Hirsh, provided valuable encouragement and advice that substantially benefited the final work. In particular, Prof. Elizabeth Page-Gould generously offered her time, advice, support, and extensive feedback on this dissertation. I also wish to acknowledge the examiners (alpha- betically): Prof. Matthew Feinberg, Prof. Will Gervais, Prof. Cendri Hutcherson, and Prof. Yoel Inbar. Ultimately, I wish to thank everyone who supported me throughout this journey. iii Contents List of Figures vii List of Tables ix 1 Introduction 1 1.1 Overview . .2 1.2 Current Work . .3 2 The Literature of Psychological Meme Science 4 2.1 Introduction . .4 2.2 Background . .4 2.2.1 Six Degrees of Separation . .4 2.2.2 Academic Networks . .5 2.2.3 Coauthorship . .7 2.2.4 Summary . .9 2.3 Methods . .9 2.3.1 Bibliographic Entries . .9 2.3.2 Bibliography Management . 11 2.3.3 Scholarship Catalog Methods . 11 2.3.4 Biographic Research . 12 2.3.5 Cleaning Coauthorship Data . 12 2.3.6 Coauthorship Network . 12 2.3.7 Network Analysis Methods . 13 2.3.8 Visualization Methods . 13 2.4 Results . 13 2.4.1 Coauthorship Network . 13 2.4.2 Main Component Network . 15 2.4.3 Component Path Length Distribution . 16 2.4.4 Community Detection . 17 2.4.5 Component Community Size Distribution . 18 2.4.6 Groups of Communities . 18 2.4.7 Community Labels . 18 2.4.8 Author Influence . 21 2.4.9 Influential Institutions . 22 iv 2.4.10 Online Interactive Viewer . 22 2.4.11 Summary of Results . 23 2.5 Discussion . 23 2.5.1 Longer Path Length . 24 2.5.2 Utility of Scholarly Silos . 25 2.5.3 Institutions . 25 2.5.4 Observations of academic publishing over time . 26 2.5.5 When to Stop . 27 2.6 Conclusion . 27 2.6.1 Future Directions . 27 2.6.2 Applying this New Knowledge . 28 3 The Analytical Scale of Online Political Campaigns 29 3.1 Introduction . 29 3.2 Background . 30 3.2.1 Democracy and Elections . 30 3.2.2 Campaigns and Electioneering . 31 3.2.3 Speaking for a Collective . 31 3.2.4 Finding Symbols in Campaign Speech . 32 3.2.5 Memes . 34 3.2.6 Questions of Scale and Causation . 34 3.2.7 Models . 34 3.2.8 Basic Conceptual Model . 41 3.3 Methods . 42 3.3.1 Data Collection . 42 3.3.2 Collecting Tweets . 44 3.3.3 Collecting the Social Graph . 45 3.3.4 Natural Language Processing . 46 3.3.5 Requirements for a Method that Identifies Memes . 46 3.3.6 Pointwise Mutual Information . 47 3.3.7 Advantages and Disadvantages of PMI . 48 3.3.8 Community Detection . 48 3.3.9 Hierarchical Linear Modelling . 48 3.3.10 Operational Terminology . 49 3.4 Results . 49 3.4.1 Tweets . 49 3.4.2 Social . 55 3.4.3 Models . 60 3.4.4 Summary of Results . 66 3.5 Discussion . 66 3.5.1 Phenomenological Scale . 66 3.5.2 Agency of the Individual . 67 3.5.3 Influence of Collectives . 68 3.5.4 Covariation of Symbols and the Unconscious . 68 v 3.5.5 Symbols that Emerge . 69 3.5.6 Misinformation and Propaganda . 70 3.6 Conclusion . 70 4 Urban Legend Propagation 71 4.1 Introduction . 71 4.2 Background . 71 4.2.1 Key Terminology . 71 4.2.2 Reviewing a study on Urban Legends . 76 4.2.3 The current work . 80 4.3 Methods . 81 4.3.1 Computational Modelling Theory . 81 4.3.2 The Current Work . ..
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