Potential cyberbullying detection in social media platforms based on a multi-task learning framework
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 comme...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2024-01-01
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| Series: | International Journal of Data and Network Science |
| Online Access: | http://www.growingscience.com/ijds/Vol8/ijdns_2023_198.pdf |
| _version_ | 1797646806826352640 |
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| author | Guo Xingyi Hamedi Mohd Adnan |
| author_facet | Guo Xingyi Hamedi Mohd Adnan |
| author_sort | Guo Xingyi |
| collection | DOAJ |
| description | 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 mechanisms 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 entirely 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. |
| first_indexed | 2024-03-11T15:07:06Z |
| format | Article |
| id | doaj.art-bb9020864b754032900bb05ef5f6edff |
| institution | Directory Open Access Journal |
| issn | 2561-8148 2561-8156 |
| language | English |
| last_indexed | 2024-03-11T15:07:06Z |
| publishDate | 2024-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj.art-bb9020864b754032900bb05ef5f6edff2023-10-30T03:55:34ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562024-01-0181253410.5267/j.ijdns.2023.10.021Potential cyberbullying detection in social media platforms based on a multi-task learning frameworkGuo XingyiHamedi Mohd Adnan 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 mechanisms 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 entirely 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.http://www.growingscience.com/ijds/Vol8/ijdns_2023_198.pdf |
| spellingShingle | Guo Xingyi Hamedi Mohd Adnan Potential cyberbullying detection in social media platforms based on a multi-task learning framework International Journal of Data and Network Science |
| title | Potential cyberbullying detection in social media platforms based on a multi-task learning framework |
| title_full | Potential cyberbullying detection in social media platforms based on a multi-task learning framework |
| title_fullStr | Potential cyberbullying detection in social media platforms based on a multi-task learning framework |
| title_full_unstemmed | Potential cyberbullying detection in social media platforms based on a multi-task learning framework |
| title_short | Potential cyberbullying detection in social media platforms based on a multi-task learning framework |
| title_sort | potential cyberbullying detection in social media platforms based on a multi task learning framework |
| url | http://www.growingscience.com/ijds/Vol8/ijdns_2023_198.pdf |
| work_keys_str_mv | AT guoxingyi potentialcyberbullyingdetectioninsocialmediaplatformsbasedonamultitasklearningframework AT hamedimohdadnan potentialcyberbullyingdetectioninsocialmediaplatformsbasedonamultitasklearningframework |