Cyberbullying Detection using Pre-Trained BERT Model
Abstract
Cyberbullying is spread across various social media platforms. It is a wrong deed in which the victim is harassed by receiving the derogatory / provocative / sensitive images or text messages by the bully. Detection of such message/post in such large platforms is very difficult and may sometimes lead to false detection. Recently, deep neural network based models have shown significant improvement over traditional models in detecting cyberbullying. Also, new and more complex deep learning architectures are being developed which are proving to be useful in various NLP tasks. Google researchers has recently developed a language learning model called BERT, which is capable of generating contextual embeddings and is also able to produce task specific embeddings for classification. A new approach is proposed to cyberbullying detection in social media platforms by using the novel pre-trained BERT model with a single linear neural network layer on top as a classifier, which improves over the existing results. The model is trained and evaluated on two social media datasets of which one dataset is small size and the second dataset is relatively larger size.
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PyTorch is a widely used framework for cyberbullying detection.
“Another widely used framework for cyberbullying detection [19,73,168,185] is PyTorch, which is developed based on the Torch [186] library of Lua programming language.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [73]
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Kurniawan, A., Indrabayu, I., & Yusuf, M. (2024) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict62357.2024.10617768 · Cited by 3 · see citing works
Multimodal Cyberbullying Detection In Videos
Suketha, M., H., Naik, J. D., Naik, P. G., Praneeth, P. B., & Lasrado, D. (2024) · International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC) · doi:10.1109/cosmic63293.2024.10871693 · Cited by 1 · see citing works
Abusaqer, M., & Saquer, J. M. (2025) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00216 · Cited by 1 · see citing works
Zhong, Y., Zhang, Z.-W., Ding, J.-H., Sun, Y., Zheng, Y., Yu, Y.-F., Guan, Z.-C., Su, S.-Z., Wu, T., Gong, Y.-M., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03605-4 · Cited by 1 · see citing works
Detection of Offensive Comments for Textual Data Using Machine Learning
Hooda, R., Jaiswal, A., Bansal, I., Jain, M., Singh, P., & Sachdeva, N. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23724-9_20 · Cited by 1 · see citing works
Detection of Cyber-Bullying Through Sentimental Analysis
Sunitharam, C., Nandini, P., & Rakshita, K. (2023) · International Journal of Soft Computing and Engineering · doi:10.35940/ijsce.a3594.0313123 · Cited by 1 · see citing works
Saichandana, B., & Kamakshi, P. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i7s.7018 · Cited by 1 · see citing works
Integrating BERT and RESNET50V2 for Multimodal Cyberbullying Detection*
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Approach to Social Media Cyberbullying and Harassment Detection Using Advanced Machine LearningPreprint
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Extraction of keywords in the abstract of scientifics papers using BERT
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Digital Tormenting Identification ON Web-based Entertainment Utilizing Machine Learning
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Cyber bullying Detection on Social Media Using Machine Learning
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Past, Present, and Future of Automatic Cyberbullying Detection Research
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A Review on Cyberbullying Detection on Twitter Dataset
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Use of Machine Learning to Identify Fake Profiles
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Detection of Cyberbullying using BiLSTM
Bhamre, A. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.60420
Multilingual Cyberbullying Classification for Social Platforms
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Realtime Cyberbullying Detection on Telegram: A Machine Learning Approach with Cyberbot
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Retracted: Cyber-Bullying Detection on Social Medial Using Machine Learning
Chavan, P. S., Musale, J. C., Thorat, R., Rede, G. S., Bora, D. D., & Dalvi, S. M. (2024) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart63812.2024.10882580
RetractedVaitla, V., Moh, M., & Moh, T.-S. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00162
Cyberbullying Detection using Deep Learning Algorithms
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- Authors
- Yadav, J., Kumar, D., & Chauhan, D.
- Affiliations
- Delhi Technological University (IN)
- ORCID
- Kumar, Devesh
- Venue
- 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 1096-1100
- OpenAlex
- W3047016663
- Cited by
- 116
- References
- 13
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 32 · 2023: 31 · 2022: 18 · 2021: 10
- FWCI
- 5.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Electronics and Sustainable Communication Systems (ICESC) (publishedVersion)
- 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 › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, BERT, pre-trained language models, social media, text classification, contextual embeddings, linear classifiers, small datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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