mlsanityData to 7 Oct 2026

Exploring Self-Identified Counseling Expertise in Online Support Forums

arXiv 2106.12976 ↗First posted 24 Jun 2021cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 52 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

6 of 52 references.

References · 6

  1. 2017Regularizing and Optimizing LSTM Language Modelscited by 205
  2. 2018An Analysis of Neural Language Modeling at Multiple Scalescited by 31
  3. 2020Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platformscited by 16
  4. 2019Finding Your Voice: The Linguistic Development of Mental Health Counselorscited by 9
  5. 2020"Trust me, I have a Ph.D.": A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communitiescited by 2
  6. 2015Thousands of Positive Reviews: Distributed Mentoring in Online Fan Communitiescited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 4; 2025: 1.

10 papers.