Exploring Self-Identified Counseling Expertise in Online Support Forums
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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
- 2017Regularizing and Optimizing LSTM Language Modelscited by 205
- 2018An Analysis of Neural Language Modeling at Multiple Scalescited by 31
- 2020Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platformscited by 16
- 2019Finding Your Voice: The Linguistic Development of Mental Health Counselorscited by 9
- 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
- 2015Thousands of Positive Reviews: Distributed Mentoring in Online Fan Communitiescited by 1
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 4; 2025: 1.
10 papers.
- 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
- 2024Large Language Models in Mental Health Care: a Scoping Reviewcited by 34
- 2025From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversationscited by 23
- 2023Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyondcited by 22
- 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategycited by 15
- 2024ESC-Eval: Evaluating Emotion Support Conversations in Large Language Modelscited by 15
- 2022Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathycited by 8
- 2024Modeling Empathetic Alignment in Conversationcited by 2
- 2024The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AIcited by 2
- 2021Modeling Proficiency with Implicit User Representationscited by 0
- 2025From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations17 Feb 2025
- 2024The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI12 Aug 2024
- 2024ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models21 Jun 2024
- 2024Modeling Empathetic Alignment in Conversation2 May 2024
- 2024Large Language Models in Mental Health Care: a Scoping Review1 Jan 2024
- 2023Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond9 Oct 2023
- 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy17 May 2023
- 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction4 May 2023
- 2022Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy15 May 2022
- 2021Modeling Proficiency with Implicit User Representations15 Oct 2021