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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
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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
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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
Fortuna, P., & Wanner, L. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102524 · Cited by 151
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CyberBERT: BERT for cyberbullying identification
Paul, S., & Saha, S. (2022) · Multimedia Systems · doi:10.1007/s00530-020-00710-4 · Cited by 116
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
Kiritchenko, S., Nejadgholi, I., & Fraser, K. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12590 · Cited by 126
Automatic Classification of Sexism in Social Networks: An Empirical Study on Twitter Data
Rodríguez-Sanchez, F. J., Carrillo‐de‐Albornoz, J., & Plaza, L. (2020) · IEEE Access · doi:10.1109/access.2020.3042604 · Cited by 121
Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach
Kocoń, J., Figas, A., Gruza, M., Puchalska, D., Kajdanowicz, T., & Kazienko, P. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102643 · Cited by 103
Al-Garadi, M. A., Yang, Y., Cai, H., Ruan, Y., O’Connor, K., Gonzalez‐Hernandez, G., Perrone, J., & Sarker, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01394-0 · Cited by 106
Khan, S., Kamal, A., Fazil, M., Alshara, M. A., Sejwal, V. K., Alotaibi, R. M., Baig, A. R., & Alqahtani, S. (2022) · IEEE Access · doi:10.1109/access.2022.3143799 · Cited by 101
Deep Learning Based Fusion Approach for Hate Speech Detection
Zhou, Y., Yang, Y., Liu, H., Liu, X., & Savage, N. (2020) · IEEE Access · doi:10.1109/access.2020.3009244 · Cited by 107
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
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Samghabadi, N. S., Patwa, P., Pykl, S., Mukherjee, P., Das, A., & Solorio, T. (2020) · Cited by 86
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Sharma, A., Kabra, A., & Jain, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102760 · Cited by 90
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Vashistha, N., & Zubiaga, A. (2020) · Information · doi:10.3390/info12010005 · Cited by 89
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Chiril, P., Pamungkas, E. W., Benamara, F., Moriceau, V., & Patti, V. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09862-5 · Cited by 102
Thapa, S., Shiwakoti, S., Shah, S. B., Adhikari, S., Veeramani, H., Nasim, M., & Naseem, U. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01428-9 · Cited by 74
A literature survey on multimodal and multilingual automatic hate speech identification
Chhabra, A., & Vishwakarma, D. K. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01051-8 · Cited by 78
GAN Memory with No ForgettingPreprint
Cong, Y., Zhao, M., Li, J., Wang, S., & Carin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07543 · Cited by 77
Transfer learning for hate speech detection in social media
Yuan, L., Wang, T., Ferraro, G., Suominen, H., & Rizoiu, M.-A. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00224-9 · Cited by 102
Deep learning for hate speech detection: a comparative study
Malik, J. S., Qiao, H., Pang, G., & van den Hengel, A. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00650-6 · Cited by 101
On Leveraging Pretrained GANs for Generation with Limited DataPreprint
Zhao, M., Cong, Y., & Carin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11810 · Cited by 87
Cross-Lingual Few-Shot Hate Speech and Offensive Language Detection Using Meta Learning
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2022) · IEEE Access · doi:10.1109/access.2022.3147588 · Cited by 83
Self-citationWhen the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection
Elsafoury, F., Katsigiannis, S., Pervez, Z., & Ramzan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3098979 · Cited by 85
Wiedemann, G., Yimam, S. M., & Biemann, C. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.213 · Cited by 80
Mansur, Z., Omar, N., & Tiun, S. B. (2023) · IEEE Access · doi:10.1109/access.2023.3239375 · Cited by 77
Hate speech detection in social media: Techniques, recent trends, and future challenges
Rawat, A., Kumar, S., & Samant, S. S. (2024) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1648 · Cited by 70
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Albladi, A., Islam, M., Das, A., Bigonah, M., Zhang, Z., Jamshidi, F., Rahgouy, M., Raychawdhary, N., Marghitu, D., & Seals, C. (2025) · IEEE Access · doi:10.1109/access.2025.3532397 · Cited by 72
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Wen, Z., Gui, L., Wang, Q., Guo, M., Yu, X., Du, J., & Xu, R. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102883 · Cited by 60
SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
Singhal, M., Ling, C., Paudel, P., Thota, P., Kumarswamy, N., Stringhini, G., & Nilizadeh, S. (2023) · 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp57164.2023.00056 · Cited by 57
Karayiğit, H., Acı, Ç. İ., & Akdağlı, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114802 · Cited by 55
Deep learning for religious and continent-based toxic content detection and classification
Abbasi, A., Javed, A. R., Iqbal, F., Kryvinska, N., & Jalil, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22523-3 · Cited by 58
Anjum, & Katarya, R. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00755-2 · Cited by 57
del Valle-Cano, G., Quijano-Sánchez, L., Liberatore, F., & Gómez, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119446 · Cited by 63
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Madukwe, K. J., Gao, X., & Xue, B. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.18 · Cited by 49
Subramanian, M., Ponnusamy, R., Benhur, S., Shanmugavadivel, K., Ganesan, A., Ravi, D., Shanmugasundaram, G. K., Priyadharshini, R., & Chakravarthi, B. R. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101404 · Cited by 52
Baydogan, C., & Alataş, B. (2021) · IEEE Access · doi:10.1109/access.2021.3102277 · Cited by 48
Yokotani, K., & Takano, M. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107099 · Cited by 45
Bagging BERT Models for Robust Aggression Identification
Risch, J., & Krestel, R. (2020) · Cited by 46
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Mosca, E., Wich, M., & Groh, G. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.8 · Cited by 43
Min, C., Lin, H., Li, X., Zhao, H., Lu, J., Yang, L., & Xu, B. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.015 · Cited by 41
Kibriya, H., Siddiqa, A., Khan, W. Z., & Khan, M. K. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109153 · Cited by 50
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Kaur, S., Singh, S., & Kaushal, S. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.05.098 · Cited by 40
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Song, G., & Huang, D. (2021) · Future Internet · doi:10.3390/fi13070163 · Cited by 38
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Yadav, Y., Bajaj, P., Gupta, R. K., & Sinha, R. (2021) · IEEE India Conference · doi:10.1109/indicon52576.2021.9691704 · Cited by 20
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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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