Content matters, context matters: unraveling behavior dynamics in an online health community for tobacco cessation.

Objectives: The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs). Materials and Methods: We utilized a mixed-methods approach comprising multidimensional qualitative coding to identify themes and communication attributes, automated text analysis leveraging advances in large language models (LLMs) to classify message content and context, and social network analysis to examine the dynamics of peer interactions in this study. Using QuitNet, an online tobacco cessation forum ( Results: OHC members expressed themselves using a variety of content and context categories such as Discussion: These findings indicate that specific combinations of communication content and interaction context are associated with peer influence and abstinence outcomes in online health communities. Conclusions: Novel behavior modeling approaches can identify latent peer interaction patterns in OHCs and advance the science of just-in-time digital behavioral interventions. Theory-enriched large language models combined with network analysis provide scalable and actionable insights for individual- and network-level strategies to support risky behavior modification such as tobacco cessation.