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

TL;DR

This paper introduces a reinforcement learning framework that identifies gaps in online mental health posts and prompts users to provide missing support attributes, significantly enhancing engagement and support quality.

Contribution

It presents a novel dataset, a hierarchical taxonomy for support attributes, and a reinforcement learning-based system for dynamic prompt generation in OMHCs.

Findings

Improved attribute elicitation across language models

Enhanced user engagement in OMHCs

Validated effectiveness through human evaluation

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.