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, with the aim 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 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior.Results. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other 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.Epidemiological evidence indicates that modifiable risky health behaviors place a substantial socioeconomic burden on human health and wellness.1 Understanding human behavior in real-time settings is essential to improving health outcomes related to these behaviors.2,3 Technological advances in connectivity offer the means to obtain potentially valuable data sets in the form of electronic traces of the activities of online social communities. These data may help us to understand the intra- and interindividual intricacies of health-related behaviors. Studies of online and offline social networks provide valuable insight into social influence, information spread, and behavioral diffusion.4–6 Most of these analyses have paid more attention to the frequency of communication between members than to its content. The content, however, is relevant to behavior change theories, which address the use of specialized content to stimulate and support individuals to achieve a desired change.7,8 Contemporary work on social media data rarely addresses this fundamental concern of behavior change theorists.Outside the context of online networks, several theories have been formulated to explain behavior change. Some, such as the Transtheoretical Model,9 belong to the intrapersonal category; others, such as Social Cognitive Theory10 and social network and support models,11 are classified as interpersonal. (Appendix A, available as a supplement to the online version of this article at http://www.ajph.org, provides an overview of the theoretical constructs.) Empirical research on the applicability of these models to behavior change of health consumers in the digital era is minimal.12 Recent research showed that participation in health issue–specific social networking sites significantly influenced social factors such as identification, perceived subjective norms, and social support, which in turn resulted in greater smoking cessation self-efficacy.13 Content inclusion in analytical models of social networks can enable us to examine the content-specific patterns of social factors underlying behavior change. Through mapping of the specific content to theories, such content inclusion can facilitate the development of network interventions for health behavior changes by harnessing the power of social relationships.Studies of QuitNet, an online social network for smoking cessation, have examined the structure of peer-to-peer communication patterns and provided insights into social integrators and opinion leaders.5,14 Previous work showed the applicability of affiliation networks to real-world diffusion networks, enriching our understanding of the affiliation-based sources of influence on individuals’ behavior. Examples include the diffusion of (1) ratification of the World Health Organization Framework Convention on Tobacco Control by comembership with an online forum among countries15; (2) gunshot victimization by co-offending with victims among Chicago, Illinois, gangsters16; (3) substance use by coparticipating in school-sponsored sports or co-identifying with the same crowd types17,18; and (4) sexual behavior by coaffiliating with venues among male sex workers.19We used affiliation networks to analyze messages for content-specific patterns of network diffusion. We took an interdisciplinary approach, integrating methods from sociobehavioral sciences, social network analytics, and biomedical informatics. We employed qualitative techniques derived from grounded theory, automated text analysis, and affiliation network analysis to investigate the communication patterns underlying human behavior in online environments. Our study had 3 major components: (1) a qualitative study of human communication within user-generated data in QuitNet, (2) computational text analysis to further extend this analysis, and (3) identification of communication patterns pertinent to behavior change in affiliation networks. We anticipate that the insights gained from this research will enhance our understanding of behavior change and will have implications for the design of sociobehavioral interventions that draw upon social influence.