The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
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“On a variety of natural language understanding tasks, Bidirectional Encoder Representation from Transformers (BERT) has produced outstanding results. In numerous studies, BERT outperformed conventional machine learning algorithms, achieved cutting-edge performance, and demonstrated promising results in the detection of cyberbullying.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [60]
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“To assess the effectiveness of BERT in detecting cyberbullying in a social media context, Mozafari et al. [60] used BERT to categorize cyberbullying in two social media datasets. In terms of precision, recall, and F1 scores, their experimental findings are encouraging when compared to the prior research in this area.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [60]
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“Studies [22,58-60] show that BERT achieved high accuracy and F1 scores to classify cyberbullying in various types of online content, such as tweets and comments.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [60]
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Cites
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100
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
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
A Survey on Automatic Detection of Hate Speech in Text
Fortuna, P., & Nunes, S. (2019) · ACM Computing Surveys · doi:10.1145/3232676 · Cited by 1,241
Abusive Language Detection in Online User Content
Nobata, C., Tetreault, J., Thomas, A. O., Mehdad, Y., & Chang, Y. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883062 · Cited by 1,197
The Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817
Cited by
Sorted by citations
Hate speech detection and racial bias mitigation in social media based on BERT model
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · PLOS ONE · doi:10.1371/journal.pone.0237861 · Cited by 260
Self-citationA Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
Qasim, R., Bangyal, W. H., Alqarni, M., & Almazroi, A. A. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/3498123 · Cited by 268
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Davani, A. M., Díaz, M., & Prabhakaran, V. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00449 · Cited by 294
Towards generalisable hate speech detection: a review on obstacles and solutions
Yin, W., & Zubiaga, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.598 · Cited by 238
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study
Pamungkas, E. W., Basile, V., & Patti, V. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102360 · Cited by 162
Metadata
- Authors
- Mozafari, M., Farahbakhsh, R., & Crespi, N.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Mozafari, Marzieh
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2019-11-26
- Volume
- Studies in Computational Intelligence book series (SCI, volume 881)
- Pages
- 928-940
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W2981478815
- Cited by
- 396
- References
- 31
- Citations by year
- 2026: 25 · 2025: 44 · 2024: 67 · 2023: 81 · 2022: 73 · 2021: 75 · 2020: 31
- FWCI
- 17.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Studies in computational intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- 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 › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- hate speech detection, BERT, transfer learning, fine-tuning, social media, offensive content, racism, sexism, Twitter
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