Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

• Published in: Journal of medical Internet research 2017-04, Vol.19 (4), p.e130-e130
• Main Authors: Wang, Xi, Zhao, Kang, Street, Nick
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Blogging - utilization; Cancer; Community involvement; Data Mining; Emotional support; Health problems; Health Services - utilization; Humans; Internet; Internet - utilization; Neoplasms - psychology; Original Paper; Patient Participation - statistics & numerical data; Peer Group; Prediction models; Retention; Self-Help Groups - utilization; Social Media - utilization; Social Support; Socioeconomic status; Supervised Machine Learning; Survival Analysis; Survivor; Survivors - psychology; Virtual communities
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/jmir.6834

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.