This dissertation examines conversations and attitudes about mental health in Twitter discourse. The research uses big data collection, machine learning classification, and social network analysis to answer the following questions 1) what mental health topics do people discuss on Twitter? 2) Have patterns of conversation changed over time? Have public messaging campaigns been able to change the conversation? 3) Does Twitter data provide insights that match the results obtained from survey and experimental data? This dissertation finds that Twitter covers a wide range of topics, largely in line with the impact that these conditions have on the population. There is evidence that stigma about mental illness and the appropriation of mental health language is declining in Twitter discourse. Additionally the conversation is heterogeneous across various self-forming communities. Finally, I find that public messaging campaigns are small in scale and difficult to evaluate. The findings suggest that policy makers have a broad audience on Twitter, that there are communities engaged with specific topics, and that more campaign activity on Twitter may generate greater awareness and engagement from populations of interest. Ultimately, Twitter data appears to be an effective tool for analysis of mental health attitudes and can be a replacement or a complement for the traditional survey methods depending on the specifics of the research question.
Table of Contents
Background and Motivation
Characterizing the Mental Health Conversation
Changes over Time
Comparing Dissertation Findings with the Literature
Conclusions, Policy Implications, Limitations, and Future Work