Sentiment Analysis for Detection of Depressive Users on Social Networks
Paper
Wigell, Christopher, Tao, Xiaohui, Li, Lin, Wang, Grace Y, Zhang, Ji and Sun, Yuan. 2024. "Sentiment Analysis for Detection of Depressive Users on Social Networks." The 11th International Conference on Behavioural and Social Computing. Harbin, China 16 - 18 Aug 2024 China. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/BESC64747.2024.10780472
| Paper/Presentation Title | Sentiment Analysis for Detection of Depressive Users on Social Networks |
|---|---|
| Presentation Type | Paper |
| Authors | Wigell, Christopher, Tao, Xiaohui, Li, Lin, Wang, Grace Y, Zhang, Ji and Sun, Yuan |
| Journal or Proceedings Title | Proceedings of the 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024) |
| Number of Pages | 7 |
| Year | 2024 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | China |
| ISBN | 9798331531904 |
| 9798331531911 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/BESC64747.2024.10780472 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10780472 |
| Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10779601/proceeding |
| Conference/Event | The 11th International Conference on Behavioural and Social Computing |
| Event Details | The 11th International Conference on Behavioural and Social Computing Delivery In person Event Date 16 to end of 18 Aug 2024 Event Location Harbin, China |
| Abstract | Depression affects approximately 121 million people worldwide and has as significant an influence on patients as it does their carers. Until recently, psychologists used paper based psychological batteries for screening. New developments in Natural Language Processing however have enabled real-time analysis, offering increases in scalability and accessibility. We built on current knowledge by developing and training several Centralised Neural Network models around a pre-labelled Reddit dataset, comparing post-length and the number of words used per post, selecting the model with the highest F1 score. Our results indicated that although some models performed better than others, neither of our metrics significantly influenced model performance, with changes explained by another factor. Our research was able to yield support for it's use within online forums at a very low cost; justifying further exploration into the use of AI tools for the screening of depression and other mood disorders. Due to a lack of publicly available natural language processing models, our study also resulted in the release of a model for further research by others. |
| Keywords | AI tools |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
| 520499. Cognitive and computational psychology not elsewhere classified | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| Wuhan University of Technology, China | |
| School of Psychology and Wellbeing | |
| National Institute of Informatics Library, Japan |
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