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III LIST OF FIGURES  VII LIST OF TABLES  VII ACKNOWLEDGMENTS  IX CHAPTER 1 : INTRODUCTION  1 POLICY AND RESEARCH QUESTIONS  4 CHAPTER 2 : BACKGROUND AND MOTIVATION  6 INTRODUCTION  6 MENTAL HEALTH AND STIGMA  6 STATE OF SOCIAL MEDIA ANALYSIS  9 NETWORK AND CASCADE ANALYSIS  10 MACHINE LEARNING AND SUPERVISED CLASSIFICATION  12 SENTIMENT ANALYSIS  12 TOPIC MODELING  14 CHAPTER 3 : METHODS  15 DATA ACQUISITION  16 Gathering Tweets about mental health  16 Gathering campaign‐relevant Tweets  19 Search results and working data sets  20 DEVELOPMENT OF CODING SCHEME  20 Reliability of coders  25 AUTOMATED CODING  26 MODEL PERFORMANCE AND SELECTION  28 MODELING APPROACH COMPARISON  33 NETWORK ANALYSIS  37 CHAPTER 4 : CHARACTERIZING THE MENTAL HEALTH CONVERSATION  40 INTRODUCTION  40 MOST MENTAL HEALTH CONTENT ON TWITTER IS SELF‐FOCUSED AND FEW TWEETS SHARE MENTAL HEALTH RESOURCES  40 APPROXIMATELY 10 PERCENT OF MENTAL HEALTH‐RELEVANT TWEETS ARE STIGMATIZING  41 NETWORK COMMUNITY CONVERSATIONS ABOUT MENTAL HEALTH VARY IN TYPES AND TOPICS OF TWEETS  42 AMONG LARGE COMMUNITIES WITH MENTAL HEALTH CONVERSATIONS, 71 PERCENT DEMONSTRATED LOW LEVELS OF STIGMA IN COMMUNITY CONVERSATIONS  44 CHAPTER 5 : CHANGES OVER TIME  47 INTRODUCTION  47 LONGITUDINAL TRENDS IN MENTAL HEALTH DISCOURSE ON TWITTER  47 CAMPAIGN TWITTER PRESENCE  49 The volume of campaign‐related Twitter activity is too low to assess whether the campaign activity is affecting the overall Twitter conversation about mental health  49 Three of the four campaigns show positive signs of engaging other Twitter users to reTweet messages  51

Tweeting About Mental Health: Big Data Text Analysis of Twitter for Public Policy