A Survey of Large Language Models in Mental Health Disorder Detection on Social Media

2025-04-03

AI Summary

This study systematically reviews the application of large language models (LLMs) in detecting a broad spectrum of mental disorders—including depression, anxiety, schizophrenia, and externalizing disorders—from social media data. Focusing on Transformer-based architectures (e.g., BERT, RoBERTa, LLaMA), it synthesizes key methodological approaches: fine-tuning, prompt engineering, zero-/few-shot learning, and multimodal alignment. The review surveys prevalent datasets, evaluation metrics, and twelve canonical application paradigms. Its primary contribution is the first integrated LLM framework for multi-diagnostic mental disorder detection, explicitly identifying cross-diagnostic generalization and model interpretability as critical bottlenecks, and proposing a clinically oriented evaluation framework. The work distills seven recurrent technical challenges and five actionable improvement directions, thereby providing theoretical foundations and a technical roadmap for developing robust, trustworthy, and regulatory-compliant AI interventions in mental health.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The detection and intervention of mental health issues represent a critical global research focus, and social media data has been recognized as an important resource for mental health research. However, how to utilize Large Language Models (LLMs) for mental health problem detection on social media poses significant challenges. Hence, this paper aims to explore the potential of LLM applications in social media data analysis, focusing not only on the most common psychological disorders such as depression and anxiety but also incorporating psychotic disorders and externalizing disorders, summarizing the application methods of LLM from different dimensions, such as text data analysis and detection of mental disorders, and revealing the major challenges and shortcomings of current research. In addition, the paper provides an overview of popular datasets, and evaluation metrics. The survey in this paper provides a comprehensive frame of reference for researchers in the field of mental health, while demonstrating the great potential of LLMs in mental health detection to facilitate the further application of LLMs in future mental health interventions.

Problem

• Utilizing LLMs for mental health detection on social media
• Exploring LLM applications in analyzing depression and anxiety
• Addressing challenges in LLM-based mental disorder detection

Innovation

• Utilizing LLMs for mental health text analysis
• Detecting diverse disorders via social media data
• Summarizing LLM methods and evaluation metrics