Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities
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Abstract (arXiv)
Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a surge in popularity as go-to platforms for seeking information and support in managing mental health needs. Platforms like Reddit offer immediate interactions with peers, granting users a vital space for seeking mental health assistance. However, the largely unregulated nature of these platforms introduces intricate challenges for both users and society at large. This study explores the factors that drive peer engagement within counseling threads, aiming to enhance our understanding of this critical phenomenon. We introduce BeCOPE, a novel behavior encoded Peer counseling dataset comprising over 10,118 posts and 58,279 comments sourced from 21 mental health-specific subreddits. The dataset is annotated using three major fine-grained behavior labels: (a) intent, (b) criticism, and (c) readability, along with the emotion labels. Our analysis indicates the prominence of ``self-criticism'' as the most prevalent form of criticism expressed by help-seekers, accounting for a significant 43% of interactions. Intriguingly, we observe that individuals who explicitly express their need for help are 18.01% more likely to receive assistance compared to those who present ``surveys'' or engage in ``rants.'' Furthermore, we highlight the pivotal role of well-articulated problem descriptions, showing that superior readability effectively doubles the likelihood of receiving the sought-after support. Our study emphasizes the essential role of OMHCs in offering personalized guidance and unveils behavior-driven engagement patterns.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 3 of its 35 references that are in the map. First posted on arXiv 4 Sep 2023.
Builds on
3 of 35 references.
References · 3
Built on by
Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classificationcited by 6
- 2025Trust Modeling in Counseling Conversations: A Benchmark Studycited by 5
- 2025M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signalscited by 3
- 2025Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communitiescited by 1
- 2025EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AIcited by 1
- 2026Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversationcited by 1
- 2026Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maximscited by 0
- 2026Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maxims1 Jun 2026
- 2026Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation7 Apr 2026
- 2025EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI15 Sep 2025
- 2025Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities22 Aug 2025
- 2025M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals21 Aug 2025
- 2025Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification25 Jan 2025
- 2025Trust Modeling in Counseling Conversations: A Benchmark Study6 Jan 2025