BACKGROUND
Online social 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 are often based on self-reported data, which is 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.
OBJECTIVE
This study aimed to determine the value in leveraging social media interactions to identify health risk behavior for men who have sex with men.
METHODS
The Gay Social Networking Analysis Program (GSNAP) was created as a preliminary framework for intelligent online 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 a 3-month period. 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.
RESULTS
Across several machine learning algorithms, offline HIV, amphetamine, and methamphetamine use were able to be identified using only social media data, with the best model providing 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.
CONCLUSIONS
To our knowledge, our study is the first implementation and empirical evaluation of a social-media based public health intervention framework in MSM. We found that social media data is 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.
Identifying substance use risk based on deep neural networks and Instagram social media data
