Content-Driven Analysis of an Online Community for Smoking Cessation: Integration of Qualitative Techniques, Automated Text Analysis, and Affiliation Networks.

Objectives. We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms , iththeaim of generating targeted intervention strategies. Methods. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de -identified peer-to -peer messages from 1423 m em b e rs , posted between March 1 and April 30, 2007. Our mixed -methods approach comprised qualitativ ecoding, automated text analysis, and affiliation network analysis to identify , visualize, and analyze content-specific communication patterns underlying smoking behavior. Results. T h em e s w e identified in QuitNet messages included relapse, QuitNetspecific tradition s , and cravings. QuitNet members who were exposed to o the r abstinent members by exchanging content related to inte personal themes (e.g., social support, traditions , progress) ten d ed to abstain. Themes found in o the r types of content did not show significant correlation with abstinence. Conclusions. Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion. [ABSTRACT FROM AUTHOR]