Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks
Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. This study uses social media and pre-trained language models to explore how user-generated data can predict mental disorder symptoms. Our study compares fou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10438433/ |
| _version_ | 1797293302911860736 |
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| author | Alireza Pourkeyvan Ramin Safa Ali Sorourkhah |
| author_facet | Alireza Pourkeyvan Ramin Safa Ali Sorourkhah |
| author_sort | Alireza Pourkeyvan |
| collection | DOAJ |
| description | Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. This study uses social media and pre-trained language models to explore how user-generated data can 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. |
| first_indexed | 2024-03-07T20:10:53Z |
| format | Article |
| id | doaj.art-70eddc03da2948ef894f74d2e3272780 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T20:10:53Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-70eddc03da2948ef894f74d2e32727802024-02-28T00:01:10ZengIEEEIEEE Access2169-35362024-01-0112280252803510.1109/ACCESS.2024.336665310438433Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social NetworksAlireza Pourkeyvan0Ramin Safa1https://orcid.org/0000-0002-1779-1019Ali Sorourkhah2https://orcid.org/0000-0002-4961-5941Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, IranDepartment of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, IranDepartment of Management, Ayandegan Institute of Higher Education, Tonekabon, IranEarly diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. This study uses social media and pre-trained language models to explore how user-generated data can 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.https://ieeexplore.ieee.org/document/10438433/Machine learningmental healthsocial networkstext miningtransformers |
| spellingShingle | Alireza Pourkeyvan Ramin Safa Ali Sorourkhah Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks IEEE Access Machine learning mental health social networks text mining transformers |
| title | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| title_full | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| title_fullStr | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| title_full_unstemmed | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| title_short | Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks |
| title_sort | harnessing the power of hugging face transformers for predicting mental health disorders in social networks |
| topic | Machine learning mental health social networks text mining transformers |
| url | https://ieeexplore.ieee.org/document/10438433/ |
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