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

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

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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Research works printed but not linked: 2

  1. 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.
  2. 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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Metadata

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
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
Licence
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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