Assessing Mental Health Signals Among Sexual and Gender Minorities using Twitter Data

Sexual and gender minorities' (SGMs) mental health needs remain little understood. Because of stigma and discrimination, SGMs are often unwilling to self-identify and reluctant to participate in traditional surveys. On the other hand, social media platforms have brought rapid changes to the health communication landscape and provided us a new data source for health surveillance of vulnerable populations. In this study, we explored machine learning methods to identify SGM individuals through finding their self-identifying tweets; then, applied a lexicon-based text analysis method to extract emotion and mental health signals from SGMs' Twitter timelines. We found that 1) SGM people have expressed more negative feelings in their tweets, and 2) within SGM populations, gay and genderfluid individuals tend to use more words related to negative emotions, anger, anxiety, and sadness in their tweets.