The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
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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“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]
Citations
Cites
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Cited by
Sorted by citations
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
“…Formspring [22], Harmful meme of COVID-19 [166], Memes of US politics [167], Image from online [168], Cyberbert: BERT for cyberbullying identification [22], Social media content [73] Theano DL models that are used in NLP tasks [175] No Works Found May not be the best choice for small-scale projects.”
BackgroundMethodInfluentialAn application to detect cyberbullying using machine learning and deep learning techniques
Raj, M., Singh, S., Solanki, K., & Selvanambi, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01308-5 · Cited by 97
Abusive Bangla comments detection on Facebook using transformer-based deep learning models
Aurpa, T. T., Sadik, R., & Ahmed, M. S. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00852-x · Cited by 91
When 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
Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT
Muneer, A., Alwadain, A. S., Ragab, M. G., & Alqushaibi, A. (2023) · Information · doi:10.3390/info14080467 · Cited by 63
Metadata
- 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
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