Cited by 115Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningTopic Modeling

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BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection

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Khan, Shakir, Fazil, Mohd, Sejwal, Vineet Kumar, Alshara, Mohammed Ali, Alotaibi, Reemiah Muneer, Kamal, Ashraf, Baig, Abdul Rauf·2022·Journal of King Saud University - Computer and Information Sciences

DOI 10.1016/j.jksuci.2022.05.006

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Abstract

Online social networks(OSNs) face the challenging problem of hate speech, which should be moderated for the growth of OSNs. The machine learning approaches dominate the existing set of approaches for hate speech detection. In this study, we introduce BiCHAT: a novel BiLSTM with deep CNN and Hierarchical ATtention-based deep learning model for tweet representation learning toward hate speech detection. The proposed model takes the tweets as input and passes through a BERT layer followed by an attention-aware deep convolutional layer. The convolutional encoded representation further passes through an attention-aware Bidirectional LSTM network. Finally, the model labels the tweet as hateful or normal through a softmax layer. The proposed model is trained and evaluated over the three benchmark datasets extracted from Twitter and outperforms the state-of-the-art (SOTA) (Khan et al., 2022; Roy et al., 2020; Ding et al., 2019) and baseline methods with an improvement of 8%,7% and 8% in terms of precision, recall, and f-score, respectively. BiCHAT also demonstrates good performance considering training and validation accuracy with an improvement of 5% and 9%, respectively. We also examined the impact of different constituting neural network components on the model. On analysis, we observed that the exclusion of the deep convolutional layer has the highest impact on the performance of the proposed model. We also investigated the efficacy of different embedding techniques, activation function, batch size, and optimization algorithms on the performance of the BiCHAT model.

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Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.4053 · Cited by 7,905
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 5,022
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919
Deep Learning for Hate Speech Detection in Tweets
Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054223 · Cited by 1,253

Show all 40 references

Research works printed but not linked: 2

Kamal, A., Abulaish, M., 2019. An lstm-based deep learning approach for detecting self-deprecating sarcasm in textual data. In: Proceedings of the 16th International Conference on Natural Language Processing (ICON’ 19), Hyderabad, India, NLPAI. pp. 201–210.
Yin, W., Kann, K., Yu, M., Schutze, H., 2017. Comparative study of cnn and rnn for natural language processing, in: arXiv:1702.01923v1, arXiv. pp. 1–7. · arXiv

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Hamzah, S., Mohd, M., & Zakaria, L. Q. (2024) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse63888.2024.11063520
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Musembi, N. K., Gakii, C., & Mugo, D. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01514-7

Authors: Khan, S., Fazil, M., Sejwal, V. K., Alshara, M. A., Alotaibi, R. M., Kamal, A., & Baig, A. R.
Affiliations: Imam Mohammad ibn Saud Islamic University (SA)
University of Limerick (IE)
Ministry of Education (IN)
Corresponding author: Khan, Shakir
ORCID: Khan, Shakir
Fazil, Mohd
Sejwal, Vineet Kumar
Alshara, Mohammed Ali
Alotaibi, Reemiah Muneer
Kamal, Ashraf
Baig, Abdul Rauf
Venue: Journal of King Saud University - Computer and Information Sciences
Publisher: Springer Science and Business Media LLC
Published: 2022-07
Volume: 34
Issue: 7
Pages: 4335-4344
ISSN: 1319-1578 (ISSN-L)
DOI: 10.1016/j.jksuci.2022.05.006
OpenAlex: W4281251145
Cited by: 115
References: 40
Citations by year: 2026: 30 · 2025: 24 · 2024: 33 · 2023: 25 · 2022: 3
FWCI: 6.99
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Journal of King Saud University - Computer and Information Sciences (publishedVersion, cc-by-nc-nd)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: hate speech detection, Bichat, bidirectional LSTM, deep convolutional neural networks, hierarchical attention, BERT, Twitter, hyperparameter sensitivity analysis

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BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection

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