Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models

2024/09/23 by Shahid Munir Shah, Shah, Shahid Munir, Syeda Anshrah Gillani +7 · 4 citations
Computer Science · Psychology · #14J26 (Secondary) #14J60 (Primary) 14F05 #Computer Vision and Pattern Recognition (cs.CV) #Digital Mental Health Interventions #F.2.2 #FOS: Computer and information sciences #I.2.7 #Mental Health via Writing #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2409.14794

openalex publication_date 2024/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of nearly 96.0 percent. The comparative analysis of the obtained results with the relevant studies in the literature shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state of the-art systems. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, and it addresses the important implications of our results for the early diagnosis of depression on several social media platforms.

Cited by

• Menta: A Small Language Model for On-Device Mental Health Prediction
• Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
• AI Models for Depressive Disorder Detection and Diagnosis: A Review
• How does depression talk on social media? Modeling depression language with relevance-based statistical language models

Related

• KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text
• A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs
• Depression Detection on Social Media with Large Language Models
• Generating Medically-Informed Explanations for Depression Detection using LLMs
• Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings