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UC Irvine Electronic Theses and Dissertations UC Irvine UC Irvine Electronic Theses and Dissertations Title The Social Complexities of Transgender Identity Disclosure on Social Media Permalink https://escholarship.org/uc/item/19c235q0 Author Haimson, Oliver Lee Publication Date 2018 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE The Social Complexities of Transgender Identity Disclosure on Social Media DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Information and Computer Science by Oliver Lee Haimson Dissertation Committee: Professor Gillian R. Hayes, Chair Professor Gloria Mark Associate Professor Andrea Forte Assistant Professor Bonnie Ruberg 2018 © 2018 Oliver Lee Haimson DEDICATION To my family, and to anyone who has been multiple while liminal. ii TABLE OF CONTENTS TABLE OF CONTENTS iii LIST OF FIGURES vi LIST OF TABLES viii ACKNOWLEDGMENTS ix CURRICULUM VITAE xiii ABSTRACT xv CHAPTER 1 Introduction 1 Transgender 4 Identity disclosure 6 Social Media 8 Liminality 14 Findings and contribution 18 CHAPTER 2 Related Work 20 Identity and self-presentation 20 Identity and life transitions 24 Life transitions and disclosure on social media 27 Disclosure and support in online health communities 33 Gender transition as identity and life transition 38 Trans identities and experiences online 40 Transgender studies and HCI 46 Relationships between disclosure, health, and social support, in trans identity contexts and beyond 50 Research gap 55 CHAPTER 3 Methods 57 Research approach 57 Tumblr transition blogs data collection 58 Tumblr transition blogs data description 61 Tumblr transition blogs data analysis 62 InterViews data collection and participant description 72 iii InterViews data analysis 75 Limitations 76 Methods summary 78 CHAPTER 4 Tumblr Transition Bloggers’ Gender Transition Processes on Social Media 79 Why people keep transition blogs 81 Perceptions of Tumblr as a space 93 How Tumblr transition blogs are different than Facebook 100 What the transition process is like 124 Trans identity disclosures are followed by increased emotional wellbeing. 131 Discussion 136 CHAPTER 5 The Separation Stage: Disclosure to Family 139 People often disclose to family members as one of their earliest transition actiVities. 141 Family disclosures are followed by decreased emotional wellbeing in the short term, but increased emotional wellbeing in the long term. 143 Support moderates the relationship between disclosure and emotional wellbeing, but only for non-family disclosures. 153 Discussion 157 CHAPTER 6 The Transition Stage: Disclosure on Facebook 160 Trans identity disclosure on Facebook is a piVotal transition moment. 163 Facebook disclosures with supportive responses are followed by increased emotional wellbeing. 167 Support from one’s Facebook network 171 Discussion 178 CHAPTER 7 The Incorporation Stage: What Happens Next 182 For some, it gets better. 185 For others, it does not get better. 190 Complicating “better” or “worse:” Intersecting life transitions and identity facets 194 The incorporation stage inVolVes moVing away from trans as primary identity. 201 Trans identity is precarious eVen beyond the incorporation stage. 206 Discussion 208 iv CHAPTER 8 Discussion: Social Media as Social Transition Machinery 212 Summary of results chapters 212 Patterns in emotional wellbeing throughout gender transition oVer time 214 Liminality, then and now 216 Liminality and social media 219 PerVasiVe liminality and non-binary gender identities 223 Rites of passage and identity transformation 224 Social media is social transition machinery for identity reconstruction as a rite of passage. 227 Implications for design 231 Conclusion 237 APPENDIX A Screenshots of Tumblr Interface 240 APPENDIX B Methodological Detail 242 Topic modeling procedures and selection 242 Dictionary-based approach to detecting trans identity disclosures 243 IteratiVe approach to building training data set for machine learning model to detect trans identity disclosures 244 APPENDIX C Descriptive Statistics 245 GLOSSARY of Trans-Related Terminology 247 REFERENCES 249 v LIST OF FIGURES Figure Page Figure 1.1. Example of trans identity disclosure post on Tumblr describing coming 6 out to a friend. Figure 1.2. Example of trans identity disclosure post on Tumblr describing coming 7 out on Facebook. Figure 1.3. Types of content users can post on Tumblr. 11 Figure 1.4. Tumblr interface for entering text content. 11 Figure 1.5. Van Gennep’s liminality framework. 15 Figure 3.1. Structure of regression models to examine relationships between trans 70 identity disclosures, emotional wellbeing, and social support. Figure 3.2 Example of emotional wellbeing over time plots used to choose interview 73 participants. Figure 3.3. Example of emotional wellbeing over time plots shown to interview 74 participants. Figure 4.1. Disclosure audience order on average. 128 Figure 4.2. Joselyn’s Important Milestones. 130 Figure 4.3. Mapping trans identity disclosures to van Gennep’s stages of liminality. 137 Figure 5.1. Van Gennep’s liminality framework. This chapter focuses on the 139 separation stage. Figure 5.2. Graph showing how emotional wellbeing changes over time on average 147 after trans identity disclosures. Figure 5.3. Average negative and positive affect over time following trans identity 150 disclosures. Figure 6.1. Van Gennep’s liminality framework. This chapter focuses on the 160 transition stage. vi Figure 7.1. Van Gennep’s liminality framework. This chapter focuses on the 182 incorporation stage. Figure 8.1. Patterns in emotional wellbeing changes over time on average 215 throughout the liminality stages. Figure A.1. Types of user interactions (“notes”) associated with a post on Tumblr: 240 likes, replies, and reblogs. Figure A.2. Tumblr blog description free-form text entry. 241 Figure A.3. Tumblr interface for switching between two blogs maintained by the 241 same user. vii LIST OF TABLES Table Page Table 3.1. Number of posts of each post type in dataset. 61 Table 4.1. Blogging motivations. 81 Table 4.2. Interviewees’ perceptions of Tumblr as a space. 94 Table 4.3. Content types interviewees described posting on Tumblr and Facebook. 101 Table 4.4. Interviewees’ described networks / audiences on Tumblr and Facebook 107 (excluding outliers). Table 4.5: Disclosure audience order on average. 129 Table 4.6. Description of models included in Table 4.7. 131 Table 4.7. Robust linear regression models showing average negative and positive 133 emotional affect in time period following posts. Table 5.1. Description of models included in Table 5.2. 143 Table 5.2. Robust linear regression models showing average negative and positive 145 emotional affect in time period following posts. Table 6.1. Description of models included in Table 6.2. 168 Table 6.2. Robust linear regression models showing average negative and positive 170 emotional affect in time period following posts. Table 8.1. Emotional wellbeing changes over time on average after trans identity 214 disclosures. Table C.1. Descriptive statistics of variables included in regression models. 245 Table C.2. Number of posts for each trans identity disclosure audience. 246 Table C.3. Number of posts per year. 246 Table C.4. Gender statistics. 246 viii ACKNOWLEDGMENTS First, thank you Gillian Hayes for your mentorship, unwavering support, and guidance throughout the years. I could not have asked for a better advisor. Thank you for giving me the freedom to take my research ideas and run with them, for always making it clear that this line of research is valuable, and for being there to listen to my ideas and help me figure out how to weave them into coherent papers. Thank you to my committee members for your thoughtful feedback and support: Andrea Forte, who has often acted as a second advisor to me, has generously included me in her research group meetings over the years, and who helped me through the most anxious times of dissertation writing and job hunting. Gloria Mark, who has always simultaneously supported and challenged me, enabling me to grow so much as a researcher over the years. Bonnie Ruberg, who has brought new insight to my dissertation work and challenged me to articulate the stakes of my work. I have also had the opportunity to work with or learn from many other brilliant faculty and industry researchers over the years, all of whom have shaped my research career in indelible ways and to whom I owe thanks. Thank you, Geof Bowker, Melissa Mazmanian, Tom Boellstorff, Paul Dourish, and Martha Feldman at UC Irvine, and Munmun De Choudhury and Amy Bruckman at Georgia Tech. Thank you to my intern mentors, Elizabeth Churchill at Google (formerly at eBay Research Labs), whom I credit for teaching me much of what I know about qualitative methods, John Tang at Microsoft Research, and Polo Chau and Bistra Dilkina at Georgia Tech. Thank you to the faculty I was able to learn from at the CHI 2017 and iConference 2017 doctoral consortia. I also wanted to give a special thanks to Sarita ix Schoenebeck, who was the first person to tell me (an undergraduate student at the time) that yes, trans identity and social media is something that makes sense to study in HCI. Thank you to the participants in the study, and to the bloggers whose data I analyzed in this research, for sharing your time and your data. I could not have done this work without you, and I acknowledge your generosity in allowing me, and the readers of this work, to learn from your experiences. Special thanks to: Denny (from the Tumblr blog Becoming Womanhood), Joselyn (from the Tumblr blog Becoming Joselyn), Cory Dostie, and Kevin Jenkins. This work was made possible thanks to the research fellowships I received throughout my Ph.D.: The National Science Foundation Graduate Research Fellowship, the Faculty Mentor Program Fellowship, the Eugene Cota-Robles Fellowship, the Rob Kling Memorial Fellowship, and the James Harvey Scholar Award. I owe gratitude to these funding agencies for believing in my research vision and giving me the tangible resources to carry it out.
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