Predicting User Response and Support Activities in Virtual Health Support Communities

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dc.contributor.authorManga, Joseph
dc.contributor.authorWang, Bin
dc.date.accessioned2020-12-24T19:37:25Z
dc.date.available2020-12-24T19:37:25Z
dc.date.issued2021-01-05
dc.description.abstractDespite growing emphasis on the factors affecting different types of supports users receive from virtual health support communities (VHSC), theoretical knowledge on how social awareness capabilities determine the extent of the support received is yet to be investigated. Adopting social awareness theory and using data collected from the COVID-19 support community on a large VHSC platform, we apply linguistic analysis to measure the impacts of three social awareness variables — social sensitivity, social insight, and social communication — on users’ response and support behaviors. The ordinary least square regression results show that social insight significantly influenced the number of replies to a post. In addition, results from the negative binomial regression also indicate that social sensitivity and social communication significantly predicted the number of support votes and thanks votes a user’s post received. The findings reveal some important research and practical implications on the need to facilitate social awareness in VHSC forums.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.371
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70985
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocial Networking and Communities
dc.subjectcommunication
dc.subjectcovid-19
dc.subjectinsight
dc.subjectsensitivity
dc.subjectvhscs
dc.titlePredicting User Response and Support Activities in Virtual Health Support Communities
prism.startingpage3047

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