Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis.
Journal:
Journal of medical Internet research
Published Date:
Aug 29, 2025
Abstract
BACKGROUND: HIV remains a global challenge, with stigma, financial constraints, and psychosocial barriers preventing people living with HIV from accessing health care services, driving them to seek information and support on social media. Despite the growing role of digital platforms in health communication, existing research often narrowly focuses on specific HIV-related topics rather than offering a broader landscape of thematic patterns. In addition, much of the existing research lacks large-scale analysis and predominantly predates COVID-19 and the platform's transition to X (formerly known as Twitter), limiting our understanding of the comprehensive, dynamic, and postpandemic HIV-related discourse.
