Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
This work addresses the issue of unanswered posts in online mental health communities, which is crucial for providing peer and expert support, though it appears incremental as it builds on existing methods for text analysis and reinforcement learning.
The paper tackles the problem of low engagement in online mental health communities by developing a framework that identifies missing support attributes in posts and prompts users to enrich them, resulting in significant improvements in attribute elicitation and user engagement as validated by empirical results and human evaluation.
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.