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

Abstract

BackgroundOnline 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.ObjectiveThis 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.MethodsWe 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.ResultsUsers’ 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.ConclusionsDetecting 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.

An investigation of Social Support Exchange and Communication Patterns among Chinese on Online Discussion Forum

“…Social support refers to an exchange of resources between individuals in order to enhance the well-being of the receiver [10]. Community psychologists have identified different types of social support, which mainly including informational support, emotional support and companionship [9]. Informational support is the transmission of information, suggestion, or guidance to the community users.…”

“…Specifically, we built classification models to determine what categories of social support each post contains. Consistent with our previous study [9], [20] and aforementioned definitions of social support, we first determined whether a post was seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), or simply inquiring about companionship (COM). The category labels are not mutually exclusive.…”

“…Users' continuous participation in an OHC can be helpful to both the users and the sustainability of the community [9]. While advancements in modern medical science can be indiscriminately applied in different countries to cure patients worldwide, it is not clear whether the research findings of OHCs based on the Western culture can be generalized to Chinese context.…”

Analyzing Empowerment Processes Among Cancer Patients in an Online Community: A Text Mining Approach (Preprint)

“…In particular, Wang et al [18] used text mining techniques to quantitatively analyze online activity related to empowerment. They found that people use online communities mainly to share their personal story and subsequently become less active on the internet.…”

“…With respect to the development of user activity over time, Wang et al [18] showed that the participation rate in online communities dropped steeply in a short time span after a user's registration, and that most participation was related to the narrative of the user's own situation. We found similar patterns in the relation between empowerment processes and user activity, suggesting that sharing one's narrative is a means to relate the position of the patient to the new situation they find themselves in, and that the 'giving back to the community' in terms of social or informational support occurs more frequently in the later stages, and among active users.…”