Disease and Disclosure: Investigating Health-Related Self-Disclosure Online

Abstract

This study examines how stigma influences patterns of health-related self-disclosure online, alongside disease severity and prevalence. As digital platforms increasingly serve as spaces for health information-seeking, understanding what drives attention to specific diseases is critical. A disease list (N = 166) was drawn from the Global Burden of Disease dataset. Disability-adjusted life years (DALYs), prevalence, and demographic data were integrated with Reddit submissions identified using LLM-assisted search terms from 2005 to 2023. Stigma scores were computed using word embeddings to capture dimensions of disgust and judgment. Multiple linear regression models assessed associations between stigma, DALYs, prevalence, and discussion volume. While stigma and prevalence were positively associated with discussion, these effects were not significant. In contrast, DALYs significantly predicted discussion volume (B = 0.45, p < 0.01). These findings offer insight as to how public attention is distributed across diseases and inform strategies to better align health interventions with population needs.