Quantifying the Effect of Informational Support on Membership Retention in Online Communities through Large-Scale Data Analytics. (September 2018) - Xing, Wanli; Goggins, Sean; Introne, Josh
Abstract: Participating in online health communities for informational support can benefit patients in various ways. For the online communities to be sustainable and effective for their participants, membership retention and commitment are important. This study explores how informational support requesting and providing by users holding different social roles (core user and periphery user) are related with participants' retention in the community. We first crawled six years of data in the WebMD fibromyalgia forum with around 200, 000 posts and 10, 000 users. Then a supervised machine learning model is trained and validated to automatically identify the requesting and providing informational support posts exchanged between the members in the community. Lastly, survival analysis was employed to quantify how the informational support requesting and providing by different social roles predicts the member's continued participation in the online community. The results reveal the different influencing mechanism of requesting and providing support from different social roles on the patients' decision to stay in the community. The findings can aid in the design of better support mechanisms to enhance member commitment in online health communities. Highlights: This paper quantifies the effect of informational support on member retention. This paper introduces large-scale data mining to study informational support. This paper helps design a support mechanism to enhance member commitment.