Superusers in social networks for smoking cessation: Analysis of demographic characteristics and posting behavior from the canadian cancer society’s smokers’ helpline online and stopsmokingcenter.net
Open accessAbstract
BACKGROUND: Online social networks are popular components of behavior-change websites. Research has identified the participation of certain network members who assume leadership roles by providing support, advice, and direction to other members. In the literature, these individuals have been variously defined as key players, posters, active users, or caretakers. Despite their identification, very little research has been conducted on the contributions or demographic characteristics of this population. For this study, we collectively categorized key players, posters, active users, and caretakers as superusers. OBJECTIVES: To analyze data from two large but distinct Web-assisted tobacco interventions (WATI) to help gain insight into superuser demographic characteristics and how they use social networks. METHODS: We extracted cross-sectional data sets containing posting behaviors and demographic characteristics from a free, publicly funded program (the Canadian Cancer Society's Smokers' Helpline Online: SHO), and a free, privately run program (StopSmokingCenter.net: SSC). RESULTS: Within the reporting period (SHO: June 26, 2008 to October 12, 2010; SSC: May 17, 2007 to October 12, 2010), 21,128 individuals registered for the SHO and 11,418 registered for the SSC. Within the same period, 1670 (7.90%) registrants made at least one post in the SHO social network, and 1627 (14.25%) registrants made at least one post in the SSC social network. SHO and SSC superusers accounted for 0.4% (n = 95) and 1.1% (n = 124) of all registrants, and 5.7% (95/1670) and 7.62% (124/1627) of all social network participants, and contributed to 34.78% (29,422/84,599) and 46.22% (61,820/133,753) of social network content, respectively. Despite vast differences in promotion and group management rules, and contrary to the beliefs of group moderators, there were no statistically significant differences in demographic characteristics between the two superuser groups. CONCLUSIONS: To our knowledge, this is the first study that compared demographic characteristics and posting behavior from two separate eHealth social networks. Despite vast differences in promotional efforts and management styles, both WATI attracted superusers with similar characteristics. As superusers drive network traffic, organizations promoting or supporting WATI should dedicate resources to encourage superuser participation. Further research regarding member dynamics and optimization of social networks for health care purposes is required.
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The 90-9-1 rule classifies user participation into three categories: Lurkers, Contributors, and Superusers, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute significantly to the content.
“The 90–9–1 rule classifies user participation into three categories; specifically, Lurkers, Contributors, and Superusers, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute significantly to the content (15, 17, 18, 22, 23).”
From Trouble with the curve: the 90-9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training (2024) · cites this paper as [17]
The Discussion (Strengths and Limitations; page unavailable) distinguishes lurkers, members who create limited posts, and superusers. The Results (General Posting Behavior and Demographic Characteristics; page unavailable) show that a small group of superusers contributed substantial content. The paper does not state the 90–9–1 rule or its 90%, 9%, and 1% breakdown; its measured percentages differ. (LLM, conf=0.85)
Experimental — accuracy not yet measuredRCMP Cadet participation in daily surveys does not differ from the general population.
“RCMP Cadet participation does not differ from the general population”
From Trouble with the curve: the 90-9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training (2024) · cites this paper as [17]
The Methods (Data Collection) describe a registration questionnaire for users of two smoking-cessation websites, and the Results (Comparison of SHO and SSC Superusers) compare those sites’ superusers. Searches for RCMP cadets and daily survey participation found no passage addressing whether cadet participation differs from that of the general population. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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“[26,28,31] These commonalities, together with evidence that day-of-week posting patterns in our OCSN parallel those identified in recent international search engine studies, [44,45] suggest our findings will have international relevance to OCSN intervention design.”
MethodResultGraham, A. L., Papandonatos, G. D., & Zhao, K. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0458-9 · Cited by 12 · see citing works
Pearson, J., Amato, M. S., Papandonatos, G. D., Zhao, K., Erar, B., Wang, X., Cha, S., Cohn, A. M., & Graham, A. L. (2018) · Addictive Behaviors · doi:10.1016/j.addbeh.2018.06.022 · Cited by 12 · see citing works
Campos, P. C. M., & Gomide, M. (2015) · Cadernos Saúde Coletiva · doi:10.1590/1414-462x201500040241 · Cited by 13 · see citing works
Cohn, A. M., Zhao, K., Cha, S., Wang, X., Amato, M. S., Pearson, J., Papandonatos, G. D., & Graham, A. L. (2017) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2017.78.665 · Cited by 7 · see citing works
“…online social networks for cessation (Bondy and Bercovitz, 2013; Brandt et al., 2013; Burri et al., 2006; Cobb et al., 2010; Cobb et al., 2013; Myneni et al., 2013; Selby et al., 2010; van Mierlo et al., 2012; Zhang et al., 2013), and none have focused on alcohol-related content within the network.”
BackgroundBeyond Efficacy: The Depth and Diversity of Current Internet Interventions
Christensen, H. M., Calear, A. L., Andersson, G., Thorndike, F. P., & Tait, R. J. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2206 · Cited by 6 · see citing works
“The paper by van Mierlo and colleagues [24] provides an analysis of the categories of users who post to discussion boards on WATIs.”
BackgroundCreating a Supportive Environment for Self-Management in Healthcare via Patient Electronic Tools
Balouchi, S., Keshavjee, K., Zbib, A., Vassanji, K., & Toor, J. (2014) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-4666-6150-9.ch008 · Cited by 6 · see citing works
The super user selection for building a sustainable online social network marketing community
Zhang, F., Li, S., & Yu, Z. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6829-0 · Cited by 5 · see citing works
Research, Innovation and Education Towards Malaria Elimination: Improving Quality of Life in Africa
de Jager, C., Kruger, T., & Tosh, C. A. (2019) · International handbooks of quality-of-life · doi:10.1007/978-3-030-15367-0_9 · Cited by 5 · see citing works
Weekly enrollment and usage patterns in an Internet smoking cessation intervention
Welding, K., León, E. D., Cha, S., Johnson, M. L., Cohen, J. E., & Graham, A. L. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.07.004 · Cited by 5 · see citing works
“Hundreds of thousands of smokers register on Internet smoking cessation programs each year (Balmford et al., 2009; McCausland et al., 2011; Nash et al., 2015; van Mierlo et al., 2012), making them an ideal environment to study patterns of health information seeking and treatment uptake.”
BackgroundYang, Q. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18179106 · Cited by 4 · see citing works
Teckchandani, T. A., Shields, R. E., Andrews, K. L., Maguire, K. Q., Jamshidi, L., Nisbet, J., Afifi, T. O., Lix, L. M., Stewart, S. H., Sauer-Zavala, S., et al. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1297953 · Cited by 3 · see citing works
Jake‐Schoffman, D. E., Seoane, M. C., Cooper, K. B., Rajoria, M., & McVay, M. A. (2022) · Psychology and Health · doi:10.1080/08870446.2022.2087073 · Cited by 3 · see citing works
“For example, in an analysis of two organic online tobacco support communities with a combined 3,297 active members, researchers found that 5.7–7.6% of members were superusers, generating 34.8–46.2% of the content posted in the group (van Mierlo et al., 2012).”
BackgroundInfluentialvan Mierlo, T., Fournier, R., Yeung, S. K., & Lahutina, S. (2025) · JMIR Research Protocols · doi:10.2196/73773 · Cited by 3 · see citing works
Self-citationRevisiting the Issue of Trust in E-Health
Sillence, E., Briggs, P., & Harris, P. R. (2017) · Advances in psychology, mental health, and behavioral studies (APMHBS) book series · doi:10.4018/978-1-5225-3241-5.ch009 · Cited by 3 · see citing works
Creating a Supportive Environment for Self-Management in Healthcare via Patient Electronic Tools
Balouchi, S., Keshavjee, K., Zbib, A., Vassanji, K., & Toor, J. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-1928-7.ch010 · Cited by 3 · see citing works
Ellis, M. (2016) · Cited by 1 · see citing works
Plunkett, F. (2013) · TRAP@NCI (National College of Ireland) · Cited by 1 · see citing works
Creating a Supportive Environment for Self-Management in Healthcare via Patient Electronic Tools
Balouchi, S., Keshavjee, K., Zbib, A., Vassanji, K., & Toor, J. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2237-9.ch021
(2016) · Elsevier eBooks · doi:10.1016/b978-1-78548-091-1.50017-8
Overcoming Health Fears: How do online peers help patients with chronic pain?
Rueger, J., Dolfsma, W. A., & Aalbers, R. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118297
- Authors
- van Mierlo, T., Voci, S., Lee, S., Fournier, R., & Selby, P.
- Affiliations
- Centre for Addiction and Mental Health (CA)
- Canadian Cancer Society (CA)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2012-06-26
- Volume
- 14
- Issue
- 3
- Pages
- e66
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3414904
- OpenAlex
- W2168211098
- Cited by
- 58
- References
- 29
- Citations by year
- 2025: 2 · 2024: 1 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 1 · 2018: 6 · 2017: 11 · 2016: 13 · 2015: 6 · 2014: 8 · 2013: 4 · 2012: 1
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Centre for Addiction and Mental Health
- University of Toronto
- Rotman School of Management, University of Toronto
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- superusers, social networking sites, smoking cessation, posting behavior, demographic factors, online support groups, user engagement, Canada
- MeSH terms
- Canada, Cross-Sectional Studies, Demography, Health Promotion, Humans, Neoplasms, Online Systems, Patient Compliance, Smoking, Social Support, Smoking Cessation
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.
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