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Social network structure of a large online community for smoking cessation

Author

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  • Cobb, N.K.
  • Graham, A.L.
  • Abrams, D.B.

Abstract

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 103592 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.

Suggested Citation

  • Cobb, N.K. & Graham, A.L. & Abrams, D.B., 2010. "Social network structure of a large online community for smoking cessation," American Journal of Public Health, American Public Health Association, vol. 100(7), pages 1282-1289.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2009.165449_1
    DOI: 10.2105/AJPH.2009.165449
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    Cited by:

    1. Yumei Li & Xiangbin Yan, 2020. "How Could Peers in Online Health Community Help Improve Health Behavior," IJERPH, MDPI, vol. 17(9), pages 1-17, April.
    2. Yuri G. Rykov & Peter A. Meylakhs & Yadviga E. Sinyavskaya, 2016. "Network Structure of an Aids-Denailists Online Community: Identifying Core Members and the Risk Group," HSE Working papers WP BRP 71/SOC/2016, National Research University Higher School of Economics.
    3. Steven L. Johnson & Hani Safadi & Samer Faraj, 2015. "The Emergence of Online Community Leadership," Information Systems Research, INFORMS, vol. 26(1), pages 165-187, March.
    4. Zhizhen Yao & Zhenni Ni & Bin Zhang & Jian Du, 2022. "Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    5. Luke J Matthews & Peter DeWan & Elizabeth Y Rula, 2013. "Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    6. Federica Duregon & Valentina Bullo & Andrea Di Blasio & Lucia Cugusi & Martina Pizzichemi & Salvatore Sciusco & Gianluca Viscioni & David Cruz-Diaz & Danilo Sales Bocalini & Alessandro Bortoletto & Fr, 2022. "The Role of Facebook ® in Promoting a Physically Active Lifestyle: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    7. Yuri Rykov & Yadviga Sinyavskaya & Olessia Koltsova, 2018. "Accumulating Social Capital In An Online Urban Network: The Effects Of User Behaviors," HSE Working papers WP BRP 83/SOC/2018, National Research University Higher School of Economics.
    8. Shelton, Rachel C. & Lee, Matthew & Brotzman, Laura E. & Crookes, Danielle M. & Jandorf, Lina & Erwin, Deborah & Gage-Bouchard, Elizabeth A., 2019. "Use of social network analysis in the development, dissemination, implementation, and sustainability of health behavior interventions for adults: A systematic review," Social Science & Medicine, Elsevier, vol. 220(C), pages 81-101.
    9. Chiew, May & Weber, Marianne F. & Egger, Sam & Sitas, Freddy, 2012. "A cross-sectional exploration of smoking status and social interaction in a large population-based Australian cohort," Social Science & Medicine, Elsevier, vol. 75(1), pages 77-86.
    10. Xiangyu Wang & Andrew High & Xi Wang & Kang Zhao, 2021. "Predicting users' continued engagement in online health communities from the quantity and quality of received support," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(6), pages 710-722, June.

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