The 1% Rule in Four Digital Health Social Networks: An Observational Study

Background In recent years, cyberculture has informally reported a phenomenon named the 1% rule, or 90-9-1 principle, which seeks to explain participatory patterns and network effects within Internet communities. The rule states that 90% of actors observe and do not participate, 9% contribute sparingly, and 1% of actors create the vast majority of new content. This 90%, 9%, and 1% are also known as Lurkers, Contributors, and Superusers, respectively. To date, very little empirical research has been conducted to verify the 1% rule. Objective The 1% rule is widely accepted in digital marketing. Our goal was to determine if the 1% rule applies to moderated Digital Health Social Networks (DHSNs) designed to facilitate behavior change. Methods To help gain insight into participatory patterns, descriptive data were extracted from four long-standing DHSNs: the AlcoholHelpCenter, DepressionCenter, PanicCenter, and StopSmokingCenter sites. Results During the study period, 63,990 actors created 578,349 posts. Less than 25% of actors made one or more posts. The applicability of the 1% rule was confirmed as Lurkers, Contributors, and Superusers accounted for a weighted average of 1.3% (n=4668), 24.0% (n=88,732), and 74.7% (n=276,034) of content. Conclusions The 1% rule was consistent across the four DHSNs. As social network sustainability requires fresh content and timely interactions, these results are important for organizations actively promoting and managing Internet communities. Superusers generate the vast majority of traffic and create value, so their recruitment and retention is imperative for long-term success. Although Lurkers may benefit from observing interactions between Superusers and Contributors, they generate limited or no network value. The results of this study indicate that DHSNs may be optimized to produce network effects, positive externalities, and bandwagon effects. Further research in the development and expansion of DHSNs is required.

[1]  H. Leibenstein Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand , 1950 .

[2]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[3]  Health-net: an interactive computer network for campus health promotion. , 1986, Journal of American college health : J of ACH.

[4]  Sid J. Schneider,et al.  Trial of an on-line behavioral smoking cessation program , 1986 .

[5]  Robert M. Sanders THE PARETO PRINCIPLE: ITS USE AND ABUSE , 1987 .

[6]  B. Uzzi,et al.  The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect , 1996 .

[7]  D. Stark,et al.  Organizing Diversity: Evolutionary Theory, Network Analysis and Postsocialism , 1997 .

[8]  A. Freeman,et al.  Revisiting prochaska and DiClemente's stages of change theory: An expansion and specification to aid in treatment planning and outcome evaluation , 2001 .

[9]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[10]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[11]  R. Bagby,et al.  Usage and Longitudinal Effectiveness of a Web-Based Self-Help Cognitive Behavioral Therapy Program for Panic Disorder , 2005, Journal of medical Internet research.

[12]  K. Novak,et al.  The guardian , 2003 .

[13]  A. Beck,et al.  The empirical status of cognitive-behavioral therapy: a review of meta-analyses. , 2006, Clinical psychology review.

[14]  A. Geiger,et al.  American Cancer Society's QuitLink: randomized trial of Internet assistance. , 2007, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[15]  S. Zbikowski,et al.  Phone and Web-Based Tobacco Cessation Treatment: Real-World Utilization Patterns and Outcomes for 11,000 Tobacco Users , 2008, Journal of medical Internet research.

[16]  Trevor van Mierlo,et al.  An online support group for problem drinkers: AlcoholHelpCenter.net. , 2008, Patient education and counseling.

[17]  Richard P Moser,et al.  Effect of Adding a Virtual Community (Bulletin Board) to Smokefree.gov: Randomized Controlled Trial , 2008, Journal of medical Internet research.

[18]  Jamie Knight,et al.  Canada personal information protection and electronic documents act , 2008 .

[19]  P. Farvolden,et al.  Using E-Health Programs to Overcome Barriers to the Effective Treatment of Mental Health and Addiction Problems , 2009 .

[20]  World Medical Association (WMA),et al.  Declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects , 2009, Journal of the Indian Medical Association.

[21]  Daniel Parent,et al.  Online Social and Professional Support for Smokers Trying to Quit: An Exploration of First Time Posts From 2562 Members , 2010, Journal of medical Internet research.

[22]  Martin Binks,et al.  Utilization Patterns and User Characteristics of an Ad Libitum Internet Weight Loss Program , 2010, Journal of medical Internet research.

[23]  Nathan K. Cobb,et al.  Social network structure of a large online community for smoking cessation. , 2010, American journal of public health.

[24]  Hsi-Peng Lu,et al.  Why people use social networking sites: An empirical study integrating network externalities and motivation theory , 2011, Comput. Hum. Behav..

[25]  Ray Jones,et al.  Using Metrics to Describe the Participative Stances of Members Within Discussion Forums , 2011, Journal of medical Internet research.

[26]  J. Cunningham Comparison of Two Internet-Based Interventions for Problem Drinkers: Randomized Controlled Trial , 2012, Journal of medical Internet research.

[27]  Andreas Schneider,et al.  “What’s Coming Next?” Epistemic Curiosity and Lurking Behavior in Online Communities , 2012 .

[28]  J. L. Bender,et al.  Online communities for breast cancer survivors: a review and analysis of their characteristics and levels of use , 2013, Supportive Care in Cancer.

[29]  John Yen,et al.  Finding influential users of an online health community: a new metric based on sentiment influence , 2012, ArXiv.

[30]  Colleen Young,et al.  Community Management That Works: How to Build and Sustain a Thriving Online Health Community , 2012, Journal of medical Internet research.

[31]  Anita L. Blanchard,et al.  Motivations in virtual health communities and their relationship to community, connectedness and stress , 2013, Comput. Hum. Behav..

[32]  All Superusers Are Not Created Equal: Contributory Patterns Observed in Four Separate Digital Health Social Networks Promoting Behavior Change , 2013 .

[33]  Sayaka Sugimoto,et al.  Support Exchange on the Internet: A Content Analysis of an Online Support Group for People Living with Depression , 2013 .

[34]  Georg von Krogh,et al.  "What's coming next?" Epistemic curiosity and lurking behavior in online communities , 2013, Comput. Hum. Behav..

[35]  India U.S. Consulate Chennai U.S. Department of Health & Human Services , 2014 .