Scale-CoT: Integrating LLM with Psychiatric Scales for Analyzing Mental Health Issues on Social Media

Mental health issues pose significant challenges worldwide, leading researchers to utilize computing techniques to analyze social media posts for early detection. Currently, Large Language Models (LLMs) represent advanced AI systems with exceptional language processing capabilities. However, their effectiveness depends on the use of appropriate prompts. The design of suitable prompts specifically for analyzing mental health issues remains underexplored. Drawing inspiration from psychiatrists who use psychiatric scales to assess mental health, we have developed scale-based chains of thought (Scale-CoT) prompts. These not only enhance the LLMs’ ability to detect mental health issues but also increase clarity and explainability by integrating professional scales. Furthermore, we demonstrate that the knowledge generated by LLMs can be effectively transferred to smaller models, resulting in a better-performing and more lightweight system. Our approach, which leverages psychiatric scales, outperforms existing methods. Our experiments indicate that combining traditional psychiatry with computational techniques is worthwhile and deserving of further research and exploration.