Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities

Abstract

Objective: Transgender people face substantial mental health disparities, and this population’s emotional wellbeing can be particularly volatile during gender transition. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods. Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. This study’s objective was to use social media data to understand average gender transition sentiment patterns. Materials and Methods: Computational sentiment analysis and statistics were used to analyze 41,066 posts from 240 Tumblr transition blogs (online spaces where transgender people document gender transitions) to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support. Results: Findings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term. After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment. Conclusions: With foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process. Social media are a novel data source for understanding transgender people’s sentiment patterns, which can help reduce mental health disparities for this marginalized population during a particularly difficult time.