All Superusers Are Not Created Equal: Contributory Patterns Observed in Four Separate Digital Health Social Networks Promoting Behavior Change
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Abstract
Background: Mirroring the Pareto Principle (also known as the 80-20 rule), a common phenomenon in digital health social networking is the 1% rule, where 90% of those who visit an online community lurk, 9% contribute infrequently, and 1% account for the vast majority of discussions. In the healthcare literature this 1% have been identified as Superusers, members of digital health social networks who assume leadership roles by providing support, advice and direction to other users. Although, the existence of Superusers has been recognized, very little is known in regards to their posting behaviors.
Methods: Data were extracted from four separate social networks run by Evolution Health: AlcoholHelpCenter.net (AHC), DepressionCenter.net (DC), PanicCenter.net (PC) and the StopSmokingCenter.net (SSC). Each social network is anonymous, free to the consumer, expert moderated, and not actively promoted. All members consented to the use of their data for research purposes.
Results: Analysis of posting behavior in all social networks revealed right-skewed distributions, meaning that, cumulatively, most members posted infrequently and that a small number author the vast majority of posts. In this study 125, 148, 156, and 156 Superusers were identified in the AHC, DC, PC and SSC, respectively. Frequency and method of interaction varied among the networks. For example, AHC Superusers posted an average of 2.3 times per thread, while SSC Superusers posted on average 1.4 times per thread. While 96% of AHC Superusers started discussions, only 66.7% of PC Superusers initiated them. Other unique and notable combinations of posting behavior will be reviewed. Also to be discussed are results from informal qualitative interviews with moderators outlining theory-based behavior change techniques designed to increase Superuser participation in light of variations in characteristics.
Conclusions: Superusers make important contributions as they produce a network effect. For those who run digital health social networks, recruiting and managing Superusers is an important task. Understanding how Superusers contribute in different settings can have a significant impact on network size. In order to optimize the management of digital health social networks, further research is required in the characteristics of all participants, discussion topics, and group dynamics.
Learning Objectives:
In this oral presentation participant will:
• Learn a mathematical model to identify Superusers
• Observe the graphical structures of four distinct social networks
• Understand the unique characteristics of Superusers
• Learn strategies designed to increase and maintain social network growth
Methods: Data were extracted from four separate social networks run by Evolution Health: AlcoholHelpCenter.net (AHC), DepressionCenter.net (DC), PanicCenter.net (PC) and the StopSmokingCenter.net (SSC). Each social network is anonymous, free to the consumer, expert moderated, and not actively promoted. All members consented to the use of their data for research purposes.
Results: Analysis of posting behavior in all social networks revealed right-skewed distributions, meaning that, cumulatively, most members posted infrequently and that a small number author the vast majority of posts. In this study 125, 148, 156, and 156 Superusers were identified in the AHC, DC, PC and SSC, respectively. Frequency and method of interaction varied among the networks. For example, AHC Superusers posted an average of 2.3 times per thread, while SSC Superusers posted on average 1.4 times per thread. While 96% of AHC Superusers started discussions, only 66.7% of PC Superusers initiated them. Other unique and notable combinations of posting behavior will be reviewed. Also to be discussed are results from informal qualitative interviews with moderators outlining theory-based behavior change techniques designed to increase Superuser participation in light of variations in characteristics.
Conclusions: Superusers make important contributions as they produce a network effect. For those who run digital health social networks, recruiting and managing Superusers is an important task. Understanding how Superusers contribute in different settings can have a significant impact on network size. In order to optimize the management of digital health social networks, further research is required in the characteristics of all participants, discussion topics, and group dynamics.
Learning Objectives:
In this oral presentation participant will:
• Learn a mathematical model to identify Superusers
• Observe the graphical structures of four distinct social networks
• Understand the unique characteristics of Superusers
• Learn strategies designed to increase and maintain social network growth
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