Revealing Intention In Health-related Peer Interactions: Implications For Optimizing Patient Engagement In Self-health Management

Abstract

Risky health behaviors such as poor diet, physical inactivity are the main contributors to the development of diabetes, one of the major causes of death and disability in the United States. Online health communities provide new avenues for individuals to efficiently manage their health conditions and adopt a positive lifestyle. So far, analysis of health-related online social exchanges has focused solely on communication content and structure of social ties, ignoring implicit user intentions underlying communication exchanges. In this paper, we propose an analytical framework to characterize communication intent, content, and social ties in online peer interactions. We integrate models from socio-behavioral sciences and linguistics with network analytics and apply it to understand Diabetes Self-Management. Results indicate the informational needs of users expressed in forms of speech acts can vary across different user engagement and disease management profiles. Implications for the design of interventions for better self-management of diabetes are discussed.