Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913513414328320 |
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| author | Shah, Shahid Munir Gillani, Syeda Anshrah Baig, Mirza Samad Ahmed Saleem, Muhammad Aamer Siddiqui, Muhammad Hamzah |
| author_facet | Shah, Shahid Munir Gillani, Syeda Anshrah Baig, Mirza Samad Ahmed Saleem, Muhammad Aamer Siddiqui, Muhammad Hamzah |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_14794 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models Shah, Shahid Munir Gillani, Syeda Anshrah Baig, Mirza Samad Ahmed Saleem, Muhammad Aamer Siddiqui, Muhammad Hamzah Computer Vision and Pattern Recognition 14J60 (Primary) 14F05, 14J26 (Secondary) F.2.2; I.2.7 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. |
| title | Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models |
| topic | Computer Vision and Pattern Recognition 14J60 (Primary) 14F05, 14J26 (Secondary) F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2409.14794 |