Topic adversarial neural network for cross-topic cyberbullying detection
With the proliferation of social media, cyberbullying has emerged as a pervasive threat, causing significant psychological harm to individuals and undermining social cohesion. Its linguistic expressions vary widely across topics, complicating automatic detection efforts. Most existing methods strugg...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-06-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2942.pdf |
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| author | Shufeng Xiong Wenzhuo Liu Bingkun Wang Yinchao Che Lei Shi |
| author_facet | Shufeng Xiong Wenzhuo Liu Bingkun Wang Yinchao Che Lei Shi |
| author_sort | Shufeng Xiong |
| collection | DOAJ |
| description | With the proliferation of social media, cyberbullying has emerged as a pervasive threat, causing significant psychological harm to individuals and undermining social cohesion. Its linguistic expressions vary widely across topics, complicating automatic detection efforts. Most existing methods struggle to generalize across diverse online contexts due to their reliance on topic-specific features. To address this issue, we propose the Topic Adversarial Neural Network (TANN), a novel end-to-end framework for topic-invariant cyberbullying detection. TANN integrates a multi-level feature extractor with a topic discriminator and a cyberbullying detector. It leverages adversarial training to disentangle topic-related information while retaining universal linguistic cues relevant to harmful content. We construct a multi-topic dataset from major Chinese social media platforms, such as Weibo and Tieba, to evaluate the generalization performance of TANN in real-world scenarios. Experimental results demonstrate that TANN outperforms existing methods in cross-topic detection tasks, significantly improving robustness and accuracy. This work advances cross-topic cyberbullying detection by introducing a scalable solution that mitigates topic interference and enables reliable performance across dynamic online environments. |
| format | Article |
| id | doaj-art-6c3310ce9ccd40e99317455efbb286f1 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-6c3310ce9ccd40e99317455efbb286f12025-08-20T02:37:17ZengPeerJ Inc.PeerJ Computer Science2376-59922025-06-0111e294210.7717/peerj-cs.2942Topic adversarial neural network for cross-topic cyberbullying detectionShufeng Xiong0Wenzhuo Liu1Bingkun Wang2Yinchao Che3Lei Shi4College of Information and Management Science, Henan Agricultural University, Zhengzhou, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou, ChinaSchool of Information Engineering, Zhengzhou College of Finance and Economics, Zhengzhou, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou, ChinaWith the proliferation of social media, cyberbullying has emerged as a pervasive threat, causing significant psychological harm to individuals and undermining social cohesion. Its linguistic expressions vary widely across topics, complicating automatic detection efforts. Most existing methods struggle to generalize across diverse online contexts due to their reliance on topic-specific features. To address this issue, we propose the Topic Adversarial Neural Network (TANN), a novel end-to-end framework for topic-invariant cyberbullying detection. TANN integrates a multi-level feature extractor with a topic discriminator and a cyberbullying detector. It leverages adversarial training to disentangle topic-related information while retaining universal linguistic cues relevant to harmful content. We construct a multi-topic dataset from major Chinese social media platforms, such as Weibo and Tieba, to evaluate the generalization performance of TANN in real-world scenarios. Experimental results demonstrate that TANN outperforms existing methods in cross-topic detection tasks, significantly improving robustness and accuracy. This work advances cross-topic cyberbullying detection by introducing a scalable solution that mitigates topic interference and enables reliable performance across dynamic online environments.https://peerj.com/articles/cs-2942.pdfCyberbullying detectionTopic adversarial neural networkTopic-invariant featuresCross-topic adaptationChinese social media |
| spellingShingle | Shufeng Xiong Wenzhuo Liu Bingkun Wang Yinchao Che Lei Shi Topic adversarial neural network for cross-topic cyberbullying detection PeerJ Computer Science Cyberbullying detection Topic adversarial neural network Topic-invariant features Cross-topic adaptation Chinese social media |
| title | Topic adversarial neural network for cross-topic cyberbullying detection |
| title_full | Topic adversarial neural network for cross-topic cyberbullying detection |
| title_fullStr | Topic adversarial neural network for cross-topic cyberbullying detection |
| title_full_unstemmed | Topic adversarial neural network for cross-topic cyberbullying detection |
| title_short | Topic adversarial neural network for cross-topic cyberbullying detection |
| title_sort | topic adversarial neural network for cross topic cyberbullying detection |
| topic | Cyberbullying detection Topic adversarial neural network Topic-invariant features Cross-topic adaptation Chinese social media |
| url | https://peerj.com/articles/cs-2942.pdf |
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