Recruiting people from diverse backgrounds to participate in health research requires intentional and culture-driven strategic efforts. In this study, we utilize publicly available Twitter posts to identify targeted populations to recruit for our HIV prevention study. Natural language processing and machine learning classification methods were used to find self-declarations of ethnicity, gender, age group, and sexually-explicit language. Using the official Twitter API we collected 47.4 million tweets posted over 8 months from two areas geo-centered around Los Angeles. Using available tools (Demographer and M3), we identified the age and race of 5,392 users as likely young Black or Hispanic men living in Los Angeles. We then collected and analyzed their timelines to automatically find sex-related tweets, yielding 2,166 users. Despite a limited precision, our results suggest that it is possible to automatically identify users based on their demographic attributes and Twitter language characteristics for enrollment into epidemiological studies.
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