Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models
This work addresses early diagnosis of depression on social media platforms, but it is incremental as it applies fine-tuning to existing models on a known task.
The study tackled depression detection from social media posts by fine-tuning GPT-3.5 Turbo and LLaMA2-7B models, achieving nearly 96.0% accuracy and outperforming existing state-of-the-art systems.
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.