Post Classification and Recommendation for an Online Smoking Cessation Community

Zhang, Mi; Yang, Christopher C.

doi:10.1007/978-3-319-68604-2_4

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Thus, computational methods have been deployed to identify social support categories from user‐generated content by extracting lexical features, sentiment features, and topic features (De Choudhury & De, 2014; Xi Wang et al, 2017; Y.‐C. Wang, Kraut, & Levine, 2012; M. Zhang & Yang, 2017). Recent studies use word embeddings (e.g., Word2Vec) (Mikolov, Chen, Corrado, & Dean, 2013) to capture both semantic and syntactic features from user‐generated content in OHCs (Khanpour, Caragea, & Biyani, 2018; S. Zhang, Grave, Sklar, & Elhadad, 2017).…”

Section: Related Workmentioning

confidence: 99%

Predicting users' continued engagement in online health communities from the quantity and quality of received support

Wang

High

et al. 2020

Asso for Info Science & Tech

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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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Section: Related Workmentioning

confidence: 99%