Abstract
A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive. In this paper, we explore the role of expertise in responses provided to help-seeking posts regarding mental health. We study the differences between (1) interactions with peers; and (2) interactions with self-identified mental health professionals. First, we show that a classifier can distinguish between these two groups, indicating that their language use does in fact differ. To understand this difference, we perform several analyses addressing engagement aspects, including whether their comments engage the support-seeker further as well as linguistic aspects, such as dominant language and linguistic style matching. Our work contributes toward the developing efforts of understanding how health experts engage with health information- and support-seekers in social networks. More broadly, it is a step toward a deeper understanding of the styles of interactions that cultivate supportive engagement in online communities.
Original language | English (US) |
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Title of host publication | Findings of the Association for Computational Linguistics |
Subtitle of host publication | ACL-IJCNLP 2021 |
Editors | Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 4467-4480 |
Number of pages | 14 |
ISBN (Electronic) | 9781954085541 |
State | Published - 2021 |
Externally published | Yes |
Event | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online Duration: Aug 1 2021 → Aug 6 2021 |
Publication series
Name | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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Conference
Conference | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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City | Virtual, Online |
Period | 8/1/21 → 8/6/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics