Social Network Structure of a Large Online Community for Smoking Cessation
Open accessAbstract
Objectives. We evaluated the social network structure of QuitNet, one of the largest online communities for behavior change, and compared its characteristics to other known social networks. Methods. Using modern network analysis methods, we identified QuitNet members who were active during a 60-day period, along with their ties. We then derived multiple subgroups, such as key players and integrators, from connections and communication patterns. Results. Among 7569 participants, we identified 103 592 connections to other members. Metrics of social network integration were associated with increased likelihood of being female, being older, having been in the system longer, and not smoking. Conclusions. The QuitNet community is a large-scale social network with the characteristics required for sustainability of social support and social influence to promote smoking cessation and abstinence. These characteristics include persistence of members over time, heterogeneity of smoking status, and evidence of rich, bidirectional communications. Some of the influential subgroups we identified may provide targets for future network-level interventions.
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Although there are some interventions that have been used by very large numbers of people, many interventions have not been widely disseminated.
“Although there are some interventions that have been used by very large numbers of people [82,83], many interventions have not been widely disseminated.”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [83]
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- Authors
- Cobb, N. K., Graham, A. L., & Abrams, D. B.
- Affiliations
- Johns Hopkins University (US)
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 2010-07
- Volume
- 100
- Issue
- 7
- Pages
- 1282-1289
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC2882421
- OpenAlex
- W2031158620
- Cited by
- 210
- References
- 46
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 5 · 2020: 16 · 2019: 6 · 2018: 16 · 2017: 11 · 2016: 32 · 2015: 24 · 2014: 29 · 2013: 21 · 2012: 18
- FWCI
- 10.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (T32-HL00787)
- National Cancer Institute (R01CA104836)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Health › Social Media in Health Education
- Keywords
- social network structure, network analysis, social support, social influence, smoking cessation, network integration, two-way communication
- MeSH terms
- Adult, Female, Humans, Male, Middle Aged, Self-Help Groups, Social Support, Systems Theory, Smoking Cessation, Internet
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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.
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