To stay or leave?: the relationship of emotional and informational support to commitment in online health support groups
Abstract
Today many people with serious diseases use online support groups to seek social support. For these groups to be sustained and effective, member retention and commitment is important. Our study examined how different types and amounts of social support in an online cancer support group are associated with participants' length of membership. We first built machine learning models to automatically identify the extent to which messages contained emotional and informational support. Agreement with human judges was high (r > 0.76). We then used these models to measure the support exchanged in 1.5 million messages. Finally, we applied quantitative event history analysis to assess how exposure to emotional and informational support predicted group members' length of subsequent participation. The results demonstrated that the more emotional support members were exposed to, the lower the risk of dropout. In contrast, informational support did not have the same strong effects on commitment. We speculate that emotional support enhanced members' relationships with one another or the group as a whole, whereas informational support satisfied members' short-term information needs.
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Emotional and informational support have been found to be most prevalent and effective in several studies of online support and social media.
“These two forms of support have been found to be most prevalent and effective in several studies of online support and social media [38,62,66,67].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [67]
Social media enables individuals to self-disclose and express their emotional and informational needs.
“Social media enables individuals to self-disclose and express their emotional and informational needs [67].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [67]
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- Authors
- Wang, Y.-C., Kraut, R., & Levine, J. M.
- Affiliations
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- Venue
- Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
- Publisher
- ACM
- Published
- 2012-02-11
- Pages
- 833-842
- OpenAlex
- W2106212012
- Cited by
- 282
- References
- 44
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 9 · 2023: 18 · 2022: 24 · 2021: 25 · 2020: 28 · 2019: 23 · 2018: 24 · 2017: 31 · 2016: 16 · 2015: 24 · 2014: 22 · 2013: 6 · 2012: 5
- FWCI
- 97.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Communication › Knowledge Management and Sharing
- Keywords
- online support groups, cancer support groups, member retention, dropout, emotional support, information support, social support, message analysis, machine learning classification, event history analysis, serious illness, online communities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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