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.
Highlights
Overview Nowadays more and more people use the Internet to satisfy their health-related needs
The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the online health community providing emotional support (PES) (OHC)
According to Shumaker and Brownell [25], social support refers to the “exchange of resources between at least two individuals perceived by the provider or the recipient to be intended to enhance the well-being of the recipient.”
Summary
Overview Nowadays more and more people use the Internet to satisfy their health-related needs. 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. Informational support is the transmission of information, suggestion, or guidance to the community users [30] The content of such a post in an OHC is usually related to advice, referrals, education, and personal experience with the disease or health problem. Companionship, known as network support, consists of chatting, humor, teasing, as well as discussions of offline activities and daily life that are not necessarily related to one’s health problems. They are sometimes referred to as “off-topic” discussions. To simplify our automated social support classification, we did not consider instrumental support in this study
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