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.

中文翻译：

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根据获得的支持的数量和质量，预测用户对在线医疗社区的持续参与

在线健康社区（OHC）已成为具有类似健康问题的人们相互交流的主要资源。它们提供了易于访问的平台，供用户通过发布来查找，接收和提供支持。利用文本挖掘和机器学习技术的优势，我们在每个帖子中确定了社交支持类型，并在OHC中确定了新用户的支持需求。我们通过测量获得的支持的数量和质量来检查用户的首次支持寻求体验。我们的结果表明，获得的支持的数量和匹配度是新用户持续参与的积极且重要的预测指标。我们的成果可以通过留住用户，为设计和管理可持续OHC提供见识。