Xingyi, Guo and Adnan, Hamedi Mohd (2024) Potential cyberbullying detection in social media platforms based on a multi-task learning framework. International Journal of Data and Network Science, 8 (1). pp. 25-34. ISSN 2561-8148, DOI https://doi.org/10.5267/j.ijdns.2023.10.021.
Full text not available from this repository.Abstract
The proliferation of online violence has given rise to a spate of malignant incidents, necessitating a renewed focus on the identification of cyberbullying comments. Text classification lies at the heart of efforts to tackle this pernicious problem. The identification of cyberbullying comments presents unique challenges that call for innovative solutions. In contrast to traditional text classification tasks, cyberbullying comments are often accompanied by subtle and arbitrary expressions that can confound even the most sophisticated classification networks, resulting in low recognition accuracy and effectiveness. To address this challenge, a novel approach is proposed that leverages the BERT pre-training model for word embedding to retain the hidden semantic information in the text. Building on this foundation, the BiSRU++ model which combines attentional mecha-nisms is used to further extract contextual features of comments. A multi-task learning framework is employed for joint training of sentiment analysis and cyberbullying detection to improve the model's classification accuracy and generalization ability. The proposed model is no longer en-tirely reliant on a sensitive word dictionary, and experimental results demonstrate its ability to better understand semantic information compared to traditional models, facilitating the identification of potential online cyberbullying comments. © 2024 by the authors; licensee Growing Science, Canada.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Attentional mechanism; BERT; Cyberbullying comment detection; Multi-task learning; Sentiment analysis |
| Subjects: | H Social Sciences > HN Social history and conditions. Social problems. Social reform |
| Divisions: | Faculty of Arts and Social Sciences > Department of Media and Communication Studies |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Jul 2024 00:54 |
| Last Modified: | 11 Jul 2024 00:54 |
| URI: | http://eprints.um.edu.my/id/eprint/44737 |
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