Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models
<|reference_start|>Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models: 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.<|reference_end|>
@article{shah2024advancing,
    title={Advancing Depression Detection on Social Media Platforms Through
  Fine-Tuned Large Language Models},
    author={Shahid Munir Shah, Syeda Anshrah Gillani, Mirza Samad Ahmed Baig,
  Muhammad Aamer Saleem, Muhammad Hamzah Siddiqui},
    journal={arXiv preprint arXiv:2409.14794},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14794},
    primaryClass={cs.CV}
}