In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.

Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversations or acting as effective assistants in the treatment with LLMs. However, the existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence. Moreover, how to automatically evaluate multi-turn dialogues within the counseling process remains an understudied area. To bridge the gap, we propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling. To fully exploit psychological counseling reports, a two-phase approach is devised to construct high-quality dialogues while a comprehensive evaluation benchmark is developed for the effective automatic evaluation of multi-turn psychological consultations. Competitive experimental results demonstrate the effectiveness of our proposed framework in psychological counseling. We open-source the datasets and model for future research.