Leveraging ChatGPT and Other NLP Methods for Identifying Risk and Protective Behaviors in MSM: Social Media and Dating apps Text Analysis

TL;DR

This study demonstrates that analyzing social media and dating app text data using advanced NLP models can effectively predict risk and protective health behaviors among MSM, supporting personalized public health strategies.

Contribution

It introduces the use of ChatGPT and other NLP embeddings to predict health behaviors in MSM from social media and dating app texts, a novel application in public health research.

Findings

Strong prediction of binge drinking (F1=0.78)

Moderate prediction of PrEP use (F1=0.64)

Potential for scalable, personalized interventions

Abstract

Men who have sex with men (MSM) are at elevated risk for sexually transmitted infections and harmful drinking compared to heterosexual men. Text data collected from social media and dating applications may provide new opportunities for personalized public health interventions by enabling automatic identification of risk and protective behaviors. In this study, we evaluated whether text from social media and dating apps can be used to predict sexual risk behaviors, alcohol use, and pre-exposure prophylaxis (PrEP) uptake among MSM. With participant consent, we collected textual data and trained machine learning models using features derived from ChatGPT embeddings, BERT embeddings, LIWC, and a dictionary-based risk term approach. The models achieved strong performance in predicting monthly binge drinking and having more than five sexual partners, with F1 scores of 0.78, and moderate performance in predicting PrEP use and heavy drinking, with F1 scores of 0.64 and 0.63. These findings demonstrate that social media and dating app text data can provide valuable insights into risk and protective behaviors and highlight the potential of large language model-based methods to support scalable and personalized public health interventions for MSM.