Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions.

BACKGROUND: Online communities provide affordable venues for behavior change. However, active user engagement holds the key to the success of these platforms. In order to enhance user engagement and in turn, health outcomes, it is essential to offer targeted interventional and informational support. OBJECTIVE: In this paper, we describe a content plus frequency framework to enable the characterization of highly engaged users in online communities and study theoretical techniques employed by these users through analysis of exchanged communication. METHODS: We applied the proposed methodology for analysis of peer interactions within QuitNet, an online community for smoking cessation. Firstly, we identified 144 highly engaged users based on communication frequency within QuitNet over a period of 16 years. Secondly, we used the taxonomy of behavior change techniques, text analysis methods from distributional semantics, machine learning, and sentiment analysis to assign theory-driven labels to content. Finally, we extracted content-specific insights from peer interactions (n=159,483 messages) among highly engaged QuitNet users. RESULTS: Studying user engagement using our proposed framework led to the definition of 3 user categories-conversation initiators, conversation attractors, and frequent posters. Specific behavior change techniques employed by top tier users (threshold set at top 3) within these 3 user groups were found to be goal setting, social support, rewards and threat, and comparison of outcomes. Engagement-specific trends within sentiment manifestations were also identified. CONCLUSIONS: Use of content-inclusive analytics has offered deep insight into specific behavior change techniques employed by highly engaged users within QuitNet. Implications for personalization and active user engagement are discussed.

Temporal Trends of Psychosociobehavioral Factors Underlying Tobacco Use: A Semi-Automated Exploratory Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

Online communities have been an integral part of tobacco cessation programs. They are rich in content, and offer insights into factors affecting an individual's behavior change efforts. We used word representation techniques to infer implicit meaning embedded in messages exchanged in a health-related online community. Our analysis of peer interactions revealed that individuals factor in safety, glamour, expense, and media projection when choosing a form of nicotine intake. When choosing pharmacotherapy techniques, individuals focus on brands, dosage, and side effects associated with each form (e.g. gums, patches). Our analysis sheds light on factors embedded in peer interactions, which might lead to opinion formation based on peer influence and knowledge dissemination in these social platforms. Such understanding enables design of high-engagement behavior change technologies, through personalization of content delivery by factoring in individual-level beliefs, behavioral state, and community-level influences.

Characterization of Temporal Semantic Shifts of Peer-to-Peer Communication in a Health-Related Online Community: Implications for Data-driven Health Promotion.

With online social platforms gaining popularity as venues of behavior change, it is important to understand the ways in which these platforms facilitate peer interactions. In this paper, we characterize temporal trends in user communication through mapping of theoretically-linked semantic content. We used qualitative coding and automated text analysis to assign theoretical techniques to peer interactions in an online community for smoking cessation, subsequently facilitating temporal visualization of the observed techniques. Results indicate manifestation of several behavior change techniques such as feedback and monitoring' and 'rewards'. Automated methods yielded reasonable results (F-measure=0.77). Temporal trends among relapsers revealed reduction in communication after a relapse event. This social withdrawal may be attributed to failure guilt after the relapse. Results indicate significant change in thematic categories such as 'social support', 'natural consequences', and 'comparison of outcomes' pre and post relapse. Implications for development of behavioral support technologies that promote long-term abstinence are discussed.