Predicting users' continued engagement in online health communities from the quantity and quality of received support
Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first-time support-seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.
|Work Title||Predicting users' continued engagement in online health communities from the quantity and quality of received support|
|License||In Copyright (Rights Reserved)|
|Publication Date||June 1, 2021|
|Publisher Identifier (DOI)||
|Deposited||July 19, 2022|
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