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Gay men in many countries are increasingly using geosocial networking applications (GSN apps), thus offering new opportunities for understanding them. This paper provides a comprehensive content analysis of posts and opinions on Blued, the world's largest gay social networking dating app, to infer and compare opinions and behavioral characteristics of gay men in different countries. Machine learning and linguistic programming approaches were used to extract themes and analyze sentiments of posts. The results show that the majority of posts are related to daily life activities, and less are related to sensitive topics. While most posts are positive or neutral, negative emotions, including anxiety, anger, and sadness, are mainly distributed in posts related to self-identification and sexual behaviors in China and to relationships in other countries. Voting items indicate that only 50.52% of the participants will take regular HIV tests while 50.2% would have casual sex when they are single. Additionally, 35.8% of the participants may try drugs when invited by friends. Our findings suggest an opportunity and necessity for researchers and public health practitioners to use open source data on GSN apps and other social medias to inform HIV interventions and to promote social inclusion for sexual minorities.

Inferring Opinions and Behavioral Characteristics of Gay Men with Large Scale Multilingual Text from Blued