Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (2025)

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.

## introduction the proliferation of online mental health communities (omhcs) has evolved the landscape of the mental health support ecosystem, providing platforms where individuals can share experiences, seek advice, and obtain suggestions or support. these digital arenas have democratized access to mental health resources, especially for those who might otherwise face barriers to traditional support systems, possibly due to stigma, cost, or scarcity of experts @xcite . despite their how do we help users in better articulation of posts to increase the chance of receiving a response? previous studies have primarily focused on either assessing community engagement @xcite @xcite or automating responses @xcite . at the same time, a rich body of prior work highlights reddit's prominence as a valuable resource for mental health research @xcite @xcite @xcite . while language models (lms) have enhanced support providers' experiences in empathy induction @xcite , a critical gap remains in directly assisting support seekers. drawing inspiration from traditional therapeutic stages, building bond, identifying root causes, evaluating consequences, and clarifying goals @xcite , our work translates these paradigms into online settings using state-of-the-art lms. with better contextual understanding, interpersonal in-sights @xcite , and explainable ai @xcite , we aim to bridge the gap by focusing on improving support seekers' experiences in omhcs. to address these gaps systematically, we first curate a novel first-of-its-kind dataset, reddme, extending the publicly available mental health corpus -becope @xcite . reddme contains 4760 posts, where each post is annotated for the spans and presence/absence of three pivotal support seeker's cues: event, effect, and requirement. next, we also propose a dedicated taxonomy of support attributes, cuetaxo, that allows controlled prompting for lms and is responsible for question generation. finally, we propose mh-copilot, a reinforcement learning (rl)-based framework tailored to enhance the posting behavior of support seekers on omhcs. mh-copilot operates on two principles: (a) assessing the support-seeker's post for missing support attributes and (b) prompting a question to better articulate their posts by using cuetaxo. figure 1 illustrates an example of how a user can be prompted for different support seekers' cue intensity levels. a reward model ensures the quality and relevance of these generations through rl. we benchmark mh-copilot against four llms: llama-3 @xcite , mistral @xcite , phi-3 @xcite , and gemma-2 @xcite . our proposed model demonstrates consistent improvements across all baselines, with a notable improvement of +27.49%, +17.80%, +2.81% and +17.54% on rouge-l, bleu-4, bertscore and meteor respectively. we further conduct human evaluation and qualitative analysis to assess the quality of generations. our contributions are summarized below: reproducibility. the dataset and code for mh-copilot foot_0 is open sourced for research purposes. ## related work we study the support-seeking behavior on omhcs. the study of psychological aspects of human behaviour and counselling dates long back @xcite . we aim to study these aspects for controlled question generation. online mental health communities (omhcs) and support provision. the increasing reliance on digital platforms for mental health support highlights the need to enhance user experience in omhcs. @xcite and @xcite developed tools to improve interactions between support seekers and peer supporters. despite progress, direct assistance for support seekers remains limited, a gap our work addresses. studies have documented barriers to offline care @xcite and stigma in help-seeking @xcite , underscoring the critical role of peer support. while sharma et al. (2021) and yang et al. (2019) have advanced empathic communication, others like tanana et al. (2019) and zhang et al. (2019) have worked on moderation and engagement. in contrast, our research focuses on enhancing responsiveness to support-seeking posts through controlled prompting. ## dataset: reddme we propose reddme, a manually annotated corpus extended from the publicly available mental health subreddit corpus, becope @xcite . becope categorizes posts into three primary categories: (a) interactive, if there are backand-forth conversations between the op (original poster) and peers (b) non-interactive, if the post engages peers, but the op does not reply to peers (c) isolated, if the post does not have any comments. we select 4, 760 posts and manually annotate them with support attributes: event, effect, and requirement on span (rationale) and intensity level. ## methodology in this work, we propose mh-copilot, a novel framework that prompts support seekers with guiding questions based on their posts to elicit improvements. our framework first identifies the presence of such attributes and further generates the required prompts via a hierarchical taxonomy, cuetaxo. to achieve this, we divide mh-copilot into four fundamental modules: (a) a contextual attributespan identifier (cspan), (b) a support attribute intensity classifier, (c) a guiding question generator, and (d) a verifier. a schematic diagram of framework is shown in figure 4 . next, we discuss each of these modules in detail. ## conclusion to address the persistent issue of low response rates on support seeker's posts in omhcs, we proposed mh-copilot, a reinforcement-learningbased framework designed to assess and enhance the clarity of support-seeking posts. our contributions include reddme, a novel dataset comprising 4, 760 posts annotated with key support attributes event, effect, and requirement at both span and intensity levels. additionally, we propose a dedicated taxonomy, cuetaxo, for controlled prompting and generation. next, we propose mh-copilot that employs cuetaxo to generate contextually relevant and tailored questions, prompting users to provide more complete and actionable information to improve their posts inorder to elicit more responses from support-providers. our extensive benchmarking against notable language models demonstrated consistent improvements across 11 metrics. we concluded our work with an exhaus-tive analysis supplemented with human evaluation.