This study investigates the impact of informational support exchanges on member retention within the WebMD fibromyalgia forum, an online health community. By analyzing six years of data involving approximately 200,000 posts from 10,000 users, the research employs a supervised machine learning model to identify instances of informational support requests and provisions. Survival analysis is then used to understand how these exchanges, based on the social roles of core and periphery users, influence members' decisions to remain active in the community. The findings highlight the varying effects of support requesting and providing from different social roles, offering insights into designing more effective support mechanisms to boost membership retention and commitment in online health communities.
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Xing, Wanli, Sean Goggins, and Josh Introne. "Quantifying the effect of informational support on membership retention in online communities through large-scale data analytics." Computers in Human Behavior 86 (2018): 227-234.
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