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BHF: BERT-based Hierarchical Attention Fusion Network for Cyberbullying Remarks Detection
Open accessAbstract
Cyberbullying remarks based on social media platforms have seriously endangered the physical and mental health of netizens. In order to purify the network environment and create a healthy atmosphere, it needs to be identified. At present, most of the existing studies only analyze the overall features of the text, while ignoring the semantic representation of words at local locations. In order to mine the richer semantic features of text, this paper proposes a BHF model, which uses the BERT pre-training model to extract global semantic feature information, and then uses the Hierarchical Attention Network (HAN) to calculate the two-dimensional space of words and short sentences. Local semantic feature information is obtained by fusing two layers of semantic feature information to obtain three-dimensional semantic feature representation of words, short sentences and full text. The experimental results show that based on the BHF model, the semantic features of the text can be better extracted on the three types of problems of cyberbullying detection, and the accuracy of cyberbullying remarks detection can be greatly improved.
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“Hierarchical Attention Networks (HAN) have demonstrated promising results at both the document and sentence levels, especially in detecting cyberbullying.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [61]
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“HANs have been used to understand the common tone of a social media message and the presence of certain bullying behaviors at the sentence level [61,118,119].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [61]
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“According to their experimental findings, the BERT and HAN (also known as BHF) combination provides a more precise semantic representation of each word, leading to higher accuracy scores.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [61]
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Chavan, V. S., & Shylaja, S. S. (2015) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2015.7275970 · Cited by 153
Multilingual Hierarchical Attention Networks for Document ClassificationPreprint
Pappas, N., & Popescu-Belis, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00896 · Cited by 96
Detecting Cyberbullying Across Multiple Social Media Platforms Using Deep Learning
Mahat, M. (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404736 · Cited by 37
Research works printed but not linked: 14
- [ 1 ] Wulczyn E , Thain N , Dixon L. Ex Machina : Personal Attacks Seen at Scale[C] . The Web Conference. International World Wide Web Conferences Steering Committee , 2017 , pp. 1391– 1399 . [1] Wulczyn E, Thain N, Dixon L. Ex Machina: Personal Attacks Seen at Scale[C]. The Web Conference. International World Wide Web Conferences Steering Committee, 2017, pp. 1391–1399.
- [ 2 ] Zhou J , Shiren Y E , Wang H. Text Sentiment Classification Based on Deep Convolutional Neural Network Model[J] . Computer Engineering , 2019 . [2] Zhou J, Shiren Y E, Wang H. Text Sentiment Classification Based on Deep Convolutional Neural Network Model[J]. Computer Engineering, 2019.
- [ 3 ] Shao L, Y Zhou , University L T . Semantic Rules and RNN Based Sentiment Classification for Online Reviews. 2019 . [3] Shao L, Y Zhou, University L T. Semantic Rules and RNN Based Sentiment Classification for Online Reviews. 2019.
- [ 4 ] Zhou Zhihua , Machine Learning(chinese) [w] Beijing : Tsinghua University Press , 2016 , pp. 30– 32 . [4] Zhou Zhihua, Machine Learning(chinese) [w] Beijing: Tsinghua University Press, 2016, pp. 30–32.
- [ 5 ] Yin D , Xue Z , Hong L. Detection of harassment on Web 2. 0[J] . Proceedings of the Content Analysis in the Web Workshop at Www , 2009 , pp. 1– 7 . [5] Yin D, Xue Z, Hong L. Detection of harassment on Web 2. 0[J]. Proceedings of the Content Analysis in the Web Workshop at Www, 2009, pp. 1–7.
- [ 6 ] Dinakar K , Reichart R , Lieberman H. Modeling the Detection of Textual Cyberbullying[C]. The Social Mobile Web, Papers from the 2011 ICWSM Workshop , Barcelona , Catalonia, Spain , July 21, 2011 . DBLP, 2011. [6] Dinakar K, Reichart R, Lieberman H. Modeling the Detection of Textual Cyberbullying[C]. The Social Mobile Web, Papers from the 2011 ICWSM Workshop, Barcelona, Catalonia, Spain, July 21, 2011. DBLP, 2011.
- [ 8 ] Zhao R , Zhou A , Mao K. Automatic detection of cyberbullying on social networks based on bullying features[C]. ACM. ACM , 2016 , pp. 1 – 6 . [8] Zhao R, Zhou A, Mao K. Automatic detection of cyberbullying on social networks based on bullying features[C]. ACM. ACM, 2016, pp. 1–6.
- [ 10 ] Wang Kun , Cui Yanpeng, Hu Jianwei . Cyberbullying Detection, Based on the FastText and Word Similarity Schemes[J] . ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) , 2020 . [10] Wang Kun, Cui Yanpeng, Hu Jianwei. Cyberbullying Detection, Based on the FastText and Word Similarity Schemes[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2020.
- [ 11 ] Iwendi C , Srivastava G , Khan S. Cyberbullying detection solutions based on deep learning architectures[J] . Multimedia Systems , 2020 . [11] Iwendi C, Srivastava G, Khan S. Cyberbullying detection solutions based on deep learning architectures[J]. Multimedia Systems, 2020.
- [ 12 ] Pitsilis G K , Ramampiaro H , Langseth H. Detecting Offensive Language in Tweets Using Deep Learn [DB/OL] . 2019 . [12] Pitsilis G K, Ramampiaro H, Langseth H. Detecting Offensive Language in Tweets Using Deep Learn [DB/OL]. 2019.
- [ 14 ] Karthik D , Roi R , Henry L. Modeling the detection of textual cyberbullying . In Workshop on The Social Mobile Web, ICWSM , 2020 . [14] Karthik D, Roi R, Henry L. Modeling the detection of textual cyberbullying. In Workshop on The Social Mobile Web, ICWSM, 2020.
- [ 15 ] Mohit Chandra , Ashwin Pathak, Eesha Dutta , Paryul Jain, Manish Gupta , Manish Shrivastava, and Ponnurangam Kumaraguru . 2020 . AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts . In Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online). International Committee on Computational Linguistics , 2020, pp. 6277– 6283 . [15] Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, and Ponnurangam Kumaraguru. 2020. AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts. In Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online). International Committee on Computational Linguistics, 2020, pp. 6277–6283.
- [ 16 ] Devlin J , Chang M W , Lee K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J] . 2018 . [16] Devlin J, Chang M W, Lee K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.
- [ 17 ] Ashish Vaswani , Noam Shazeer, Niki Parmar , Jakob Uszkoreit, Llion Jones , Aidan N. Gomez , Łukasz Kaiser, and Illia Polosukhin , 2017 , pp. 6000 – 6010 . [17] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017, pp. 6000–6010.
Cited by
Sorted by citations
A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103
“To better represent the meanings of the semantics of the words, Feng et al. [61] suggested a BHF model that makes use of BERT and a Hierarchical Attention Network (HAN).”
BackgroundMethodCloud-Based Offensive Code Mixed Text Classification Using Hierarchical Attention Network
Karthik, D., Natarajan, R., Bhavani, R., & Rajalakshmi, D. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-1694-8.ch012
Multimodal Deep Learning for Robust Cyberbullying Detection on Online Platforms: An Overview
Chavan, A. M., & Deshmukh, R. J. (2025) · International Conference on Future Technologies (ICFT) · doi:10.1109/icft66708.2025.11336674
Stacking Ensemble vs IndoBERT for Indonesian Cyberbullying Detection
Justin, J., Howen, K., Wijaya, A., Jabar, B. A., & Halim, R. (2026) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech202671078.2026.11699022
Metadata
- Authors
- Feng, Z., Su, J., & Cao, J.
- Affiliations
- Hainan Normal University (CN)
- Ministry of Education of the People's Republic of China (CN)
- Venue
- Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing
- Publisher
- ACM
- Published
- 2022-12-23
- Pages
- 1-7
- OpenAlex
- W4323520214
- Cited by
- 4
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Machine Learning and Natural Language Processing (MLNLP) (publishedVersion)
- Funders
- Haikou Key Science and Technology Program (2020-032)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, BERT, hierarchical attention network, attention fusion, semantic feature representation, social media text
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