Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities

• URL: http://arxiv.org/abs/2508.16788v1
• Date: Fri, 22 Aug 2025 20:40:05 GMT
• Title: Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
• Abstract summary: We present a novel framework that identifies gaps and prompts users to enrich their posts, thereby improving engagement.<n>Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information.<n> Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement.
• Score: 18.36818914416437
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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