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.

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Work Title Predicting users' continued engagement in online health communities from the quantity and quality of received support
Access
Open Access
Creators
  1. Xiangyu Wang
  2. Andrew High
  3. Xi Wang
  4. Kang Zhao
Keyword
  1. Social support
  2. Support match
  3. Text mining
  4. User engagement
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Journal of the Association for Information Science and Technology
Publication Date December 3, 2020
Publisher Identifier (DOI)
  1. https://doi.org/10.1002/asi.24436
Deposited July 19, 2022

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Version 1
published

  • Created
  • Added OHC-social_support_matching_3rd.pdf
  • Added Creator Xiangyu Wang
  • Added Creator Andrew High
  • Added Creator Xi Wang
  • Added Creator Kang Zhao
  • Published
  • Updated Keyword, Publisher, Publication Date Show Changes
    Keyword
    • Social support, Support match, Text mining, User engagement
    Publisher
    • Journal of the American Society for Information Science and Technology
    • Journal of the Association for Information Science and Technology
    Publication Date
    • 2021-06-01
    • 2020-12-03
  • Updated