Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

2025-03-03

AI Summary

This study addresses early detection, severity assessment, and personalized intervention recommendation for mental health disorders in social media text (e.g., Reddit). Methodologically, it introduces a hybrid LLM-NLP framework featuring a novel LLM-driven label distillation mechanism that integrates rule-based annotation with domain-adapted fine-tuning of BERT/RoBERTa, while leveraging Llama/GPT-series models to enhance semantic understanding, domain knowledge injection, and interpretable output generation. Compared to baselines, the framework achieves a 7.2% absolute improvement in F1-score for depression and anxiety classification, supports fine-grained severity stratification, and generates evidence-informed behavioral recommendations. Experimental evaluation on real-world Reddit data demonstrates its clinical utility for auxiliary decision-making and feasibility for early warning. The approach provides a scalable, interpretable, and clinically grounded technical pathway for digital mental health interventions.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.

Problem

• Detect mental health disorders from social media discussions.
• Assess severity and recommend therapeutic interventions.
• Improve classification accuracy using hybrid NLP and LLMs.

Innovation

• Uses NLP and Generative AI for mental health detection
• Fine-tunes pre-trained models with LLM predictions
• Combines rule-based and advanced NLP for classification