Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective
Open accessAbstract
BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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OHCs are the major source of informational, social, and emotional support for people with health problems, and members interact with other stakeholders to seek, receive, and provide these types of support.
“OHCs are the major source of informational, social, and emotional support for people with health problems, and members of such communities interact with other stakeholders to seek, receive, and provide different types of supports (informational, social, and emotional) [4,70,82].”
From Improving the Theoretical Understanding Toward Patient-Driven Health Care Innovation Through Online Value Cocreation: Systematic Review (2020) · cites this paper as [4]
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References 59 · Cited by 182
Research works printed but not linked: 2
- [42]Yang J Wei X Ackerman M Adamic L Activity lifespan: an analysis of user survival patterns in online knowledge sharing communities ICWSM 2010 - Proceedings of the 4th International AAAI Conference on Weblogs and Social Media 2010 4th International AAAI Conference on Weblogs and Social Media May 23-26, 2010 Washington, DC 186 93
- [58]Street N Mangasarian O Wolberg W An inductive learning approach to prognostic prediction Proceedings of the Twelfth International Conference on Machine Learning 1995 12th International Conference on Machine Learning 1995 San Francisco, CA 522 30
- Authors
- Wang, X., Zhao, K., & Street, N.
- Affiliations
- University of Iowa (US)
- Corresponding author
- Zhao, Kang
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-04-24
- Volume
- 19
- Issue
- 4
- Pages
- e130
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5422656
- OpenAlex
- W2610156881
- Cited by
- 182
- References
- 59
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 17 · 2023: 20 · 2022: 37 · 2021: 22 · 2020: 17 · 2019: 14 · 2018: 15 · 2017: 2
- FWCI
- 21.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Iowa Research Online (The University of Iowa) (submittedVersion, cc-by)
- Iowa Research Online (The University of Iowa) (submittedVersion, other-oa)
- Funders
- National Natural Science Foundation of China (71572013)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Communication › Knowledge Management and Sharing
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Keywords
- online health communities, user participation, user churn prediction, social support, information support, emotional support, companionship, cancer survivors, user retention, survival analysis
- MeSH terms
- Supervised Machine Learning, Health Services, Humans, Neoplasms, Patient Participation, Peer Group, Self-Help Groups, Social Support, Survival Analysis, Survivors, Internet, Blogging, Data Mining, Social Media
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