Speak out! Unveiling the impact of self-disclosure on user engagement in online health communities

Purpose Online health communities (OHCs) have become essential platforms for users to share health-related information and manage self-care. A central element in these interactions is self-disclosure—the sharing of personal health information—which plays a critical role in fostering user engagement and mutual support. However, the mechanisms by which self-disclosure drives engagement, as well as the factors that may amplify these effects, remain underexamined. This study aims to examine how self-disclosure within OHCs impacts user engagement, with particular focus on the moderating effect of social capital in this relationship. Design/methodology/approach To quantitatively assess self-disclosure, we applied deep learning algorithms and named entity recognition to a dataset of 152,706 threads from an OHC. Grounded in Social Penetration Theory (SPT), we developed an empirical model to measure the breadth and depth of self-disclosure and analyzed their impact on user engagement, with social capital as a moderating variable. Findings Our findings indicate that both the breadth (variety of topics) and depth (level of detail) of self-disclosure positively influence user engagement. Additionally, social capital significantly enhances the impact of self-disclosure on engagement within OHCs. Originality/value By extending the application of SPT to OHCs, this study not only deepens our understanding of how self-disclosure functions in health-focused online communities but also provides actionable insights to improve self-disclosure practices. These findings offer practical guidance for community managers aiming to foster more effective engagement strategies within OHCs.