Abstract
BackgroundSocial media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets.ObjectiveThis study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men.MethodsThe Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages.ResultsOffline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6%, 85.9%, and 85.3%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control.ConclusionsTo our knowledge, our study is the first empirical evaluation of a social media–based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media–based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media–based just-in-time adaptive intervention to target substance use and HIV risk behavior.
Highlights
Men who have sex with men are disproportionately affected by HIV and other sexually transmitted infections
We found that social media data were correlated with offline sexual health and substance use, verified through biological testing
The proof of concept and initial results validate that public health interventions can use social media–based systems to successfully determine offline health risk behaviors
Summary
Men who have sex with men are disproportionately affected by HIV and other sexually transmitted infections. Research suggests substance use is highly associated with high-risk sexual behaviors such as condomless anal sex, multiple sex partners, and sex trading for drugs [4,5,6]. Web-based communication tools such as social networking sites (eg, “hookup apps,” dating websites) have been used among men who have sex with men to seek sexual partners and share information and resources about substance use [7,8,9,10]. Men who have sex with men who identify sexual partners online have a greater likelihood of substance use [13], the evidence is equivocal [8,14,15]. Further studies are needed to provide empirical evidence of the association between online social networking technologies and offline sexual and substance use behaviors. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that determines health behavior risk and suggests appropriate intervention targets
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