ArticleJournal of medical Internet research2017
Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Uncovering the hierarchical determinants of continuous usage in online health communities: integrating meta-analysis with FUZZY-DEMATEL-AISM.Scientific reports · 2026Pooled it
- Improving the Theoretical Understanding Toward Patient-Driven Health Care Innovation Through Online Value Cocreation: Systematic Review.Journal of medical Internet research · 2020Pooled it
- The Antecedents and Consequences of Health Care Professional-Patient Online Interactions: Systematic Review.Journal of medical Internet research · 2019Pooled it
- The Role of Seed Users in Nurturing an Online Health Community for Smoking Cessation Among People With HIV/AIDS.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2024Trial
- Web-Based Intervention for Women With Type 1 Diabetes in Pregnancy and Early Motherhood: Critical Analysis of Adherence to Technological Elements and Study Design.Journal of medical Internet research · 2018Trial
- Social Support Mechanisms in an Online Type 1 Diabetes Community: Social Network Analysis of Stakeholder Diversity and Disease Duration.Journal of medical Internet research · 2026Observational
- Emotion Expression in Breast Cancer Support Seeking: Empirical Study of an Online Community.JMIR medical informatics · 2026Article
- Mapping Characteristics, Applications, and Implementation Challenges of Virtual Communities in Cancer Care: NASSS Framework-Informed Scoping Review.Journal of medical Internet research · 2025Article
- Analysis of the Behavioral Performance and Social Support of Patients in Online Health Communities From User Profile Perspectives: Comparative Study.Journal of medical Internet research · 2025Article
- Women's Educating and Coping Strategies for Cultivating Supportive Web-Based Spaces for Discussing Sexual and Reproductive Health: Co-Design Study.Journal of medical Internet research · 2025Article
- Communal Load Sharing of Miscarriage Experiences: Thematic Analysis of Social Media Community Support.Journal of medical Internet research · 2024Article
- The Role of Technology in Online Health Communities: A Study of Information-Seeking Behavior.Healthcare (Basel, Switzerland) · 2024Article
- Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum.Journal of medical Internet research · 2023Article
- Exploring Language Used in Posts on r/birthcontrol: Case Study Using Data From Reddit Posts and Natural Language Processing to Advance Contraception Research.Journal of medical Internet research · 2023Article
- Configuration Path Study of Influencing Factors on Health Information-Sharing Behavior among Users of Online Health Communities: Based on SEM and fsQCA Methods.Healthcare (Basel, Switzerland) · 2023Article
- Factors Affecting Users' Continuous Usage in Online Health Communities: An Integrated Framework of SCT and TPB.Healthcare (Basel, Switzerland) · 2023Article
- The Effects of Patient Health Information Seeking in Online Health Communities on Patient Compliance in China: Social Perspective.Journal of medical Internet research · 2023Article
- Social Support in a Diabetes Online Community: Mixed Methods Content Analysis.JMIR diabetes · 2023Article
- Mining and analysing online social networks: Studying the dynamics of digital peer support.MethodsX · 2023Article
- Online health communities influence people's health behaviors in the context of COVID-19.PloS one · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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