English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Despite 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. © 2021 IEEE Computer Society. All rights reserved.

proceedings of the ... annual hawaii international conference on system sciences/proceedings of the annual hawaii international conference on system sciences

Predicting User Response and Support Activities in Virtual Health Support Communities