  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.
