Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917600299057152 |
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| author | Pourkeyvan, Alireza Safa, Ramin Sorourkhah, Ali |
| author_facet | Pourkeyvan, Alireza Safa, Ramin Sorourkhah, Ali |
| contents | Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16891 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks Pourkeyvan, Alireza Safa, Ramin Sorourkhah, Ali Information Retrieval Artificial Intelligence Human-Computer Interaction I.2.7; J.3 Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task. |
| title | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction I.2.7; J.3 |
| url | https://arxiv.org/abs/2306.16891 |