Advancing depression detection on social media platforms through fine-tuned large language models

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 96.4%. The comparative analysis of the obtained results with the relevant studies in the literature and the base models shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state-of-the-art systems and the base models. 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, along with the dataset description and comparative summary of the results, indicating the important implications of the obtained results for the early diagnosis of depression on various social media platforms.