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    <abstract>Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model’s effectiveness in real-world OMHC settings.</abstract>
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%0 Conference Proceedings
%T Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
%A Gaur, Bhagesh
%A Gupta, Karan
%A Srivastava, Aseem
%A Gupta, Manish
%A Akhtar, Md Shad
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F gaur-etal-2025-assess
%X Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model’s effectiveness in real-world OMHC settings.
%U https://aclanthology.org/2025.findings-emnlp.982/
%P 18102-18118

Markdown (Informal)

[Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities](https://aclanthology.org/2025.findings-emnlp.982/) (Gaur et al., Findings 2025)

• Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (Gaur et al., Findings 2025)

ACL

• Bhagesh Gaur, Karan Gupta, Aseem Srivastava, Manish Gupta, and Md Shad Akhtar. 2025. Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 18102–18118, Suzhou, China. Association for Computational Linguistics.