From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities

Carron‐Arthur, Bradley; Ali, Kathina; Cunningham, John A.; Griffiths, Kathleen M

doi:10.2196/jmir.4705

Cited by 52 publications

(60 citation statements)

References 34 publications

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“…As with other research [82] we found that even when there was connection between online and offline spaces, it appeared that users spent time censoring and editing information that they shared in order to create the most engaging identity to present to others. In this study, similar to others [73, 83,84], we found that users took on a range of roles and 15 participative stances. For example, we found evidence of protagonists, experts, befrienders, soothsayers, people who always tried to respond, witty people, sarcastic people and some evidence of lurkers [36,47].…”

Section: Comparison With Prior Worksupporting

confidence: 84%

Prevention of HIV and Other Sexually Transmissible Infections in Expatriates and Traveler Networks: Qualitative Study of Peer Interaction in an Online Forum (Preprint)

Crawford¹,

Maycock²,

Tobin³

et al. 2018

Preprint

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Background: In high income countries such as Australia an increasing proportion of HIV diagnoses have been acquired overseas including amongst expatriates and travelers. Australia's national strategies have highlighted the need for public health interventions for priority populations, including men who travel overseas frequently for work or leisure. One approach is to expand efforts to places or spaces where expatriate communities reside. Accordingly, online settings such as forums used by expatriates and travelers may have potential for prevention of HIV or other STIs with those hard to reach through more traditional interventions.

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Section: Comparison With Prior Worksupporting

confidence: 84%

Prevention of HIV and Other Sexually Transmissible Infections in Expatriates and Traveler Networks: Qualitative Study of Peer Interaction in an Online Forum (Preprint)

Crawford¹,

Maycock²,

Tobin³

et al. 2018

Preprint

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“…To address this issue, some research has sought to define actor roles (Carron-Arthur et al 2015; Cleary and Stanton 2015; Cunningham et al 2008; Jones et al 2011; Selby et al 2010; van Mierlo et al 2012). By systematically categorizing participants, taxonomies can give insight into how various actors in complex networks function in relation to one another.…”

Section: Introductionmentioning

confidence: 99%

Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks

Mierlo

Hyatt

Ching

2016

Netw Model Anal Health Inform Bioinforma

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Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8–2323), posts (29–28,684), and Gini coefficients (0.15–0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527–0.835, p < .001), and Gini coefficients and number of posts (0.342–0.725, p < .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276, p < .036). Linear regression models had positive but mixed R 2 results (0.333–0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant (t = 3.346–7.381, p < .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks (t = −4.305 and −5.934, p < .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required.

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“…One possible explanation for this interesting result is that high reputations may prevent a member from posting and making efforts because he/she can easily get the answers he/she wants. Studies about participatory patterns in OHCs showed that there are two types of users: help-seekers and influential users [71]. When a member initially joins an OHC, most likely, he/she is a help-seeker.…”

Section: Discussionmentioning

confidence: 99%

Users’ Participation Motivation and Behavior Patterns in Online Health Community: A Game Theory Viewpoint

Shang¹

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Online health communities (OHC) are one of the most promising health-related social media services that have been developed, increasing in numbers and users in the past decade. Studies show that patients can benefit from participating in OHC, including obtaining information and knowledge, receiving support, and releasing mental stress. The purpose of this study is to identify the motivation behind users' participation and to understand their behavior patterns across time in the online health community. A game theoretic model is used.

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From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities

Cited by 52 publications

References 34 publications

Prevention of HIV and Other Sexually Transmissible Infections in Expatriates and Traveler Networks: Qualitative Study of Peer Interaction in an Online Forum (Preprint)

Prevention of HIV and Other Sexually Transmissible Infections in Expatriates and Traveler Networks: Qualitative Study of Peer Interaction in an Online Forum (Preprint)

Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks

Users’ Participation Motivation and Behavior Patterns in Online Health Community: A Game Theory Viewpoint

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