Characterizing Transgender Health Issues in Twitter

TL;DR

This study uses computational analysis of Twitter data to identify health-related topics and information needs among transgender individuals, revealing linguistic and topical differences between transgender men and women to inform healthcare strategies.

Contribution

It introduces a novel computational framework for analyzing social media data to understand health issues and information needs of transgender populations at a large scale.

Findings

Identified 54 health-related topics grouped into 7 categories.

Discovered linguistic differences between transgender men and women.

Provided insights for targeted health interventions.

Abstract

Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.