Characterizing Physical Activity in a Health Social Network

New horizons are emerging within healthcare delivery, education, intervention provision, and tracking. We study a health social network that has tracked physical activities, biomarkers, and posts the participants have shared, throughout a one-year program. The program was aimed at helping people to adopt healthy behaviors and to lose weight. In this paper, we focus on users' posts that relate to physical activities. Prior papers characterize health based solely on users' information disclosed through natural language or questionnaires. The drawback of these works is their lack of medical records or health-related information to validate their findings. By contrast, with our direct access to users' physical and medical data, we investigate the implication of users' posts at both individual and group levels. We are able to validate our hypotheses about the effects of certain social network activities, by contextualizing them in the specific users' actual medical progress and documented levels of exercise. Our findings show that activity self-disclosure posts are good indicators of one's real-world physical activity, which makes them good resources for monitoring the participants. In addition, using a physical activity propagation model, we show how these posts can influence the physical activity behavior at the network level. Further, posts exhibit distinctive affective, biological, and linguistic style markers. We observe that these characteristics can be used in a predictive capacity, to detect positive activity signals with ~88% accuracy, which can be utilized for an unobtrusive monitoring solution.