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]
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“…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.”
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Integrating BERT and RESNET50V2 for Multimodal Cyberbullying Detection*
Musyoka, I., Wandeto, J., & Kituku, B. (2024) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds62089.2024.10756337 · Cited by 1
Approach to Social Media Cyberbullying and Harassment Detection Using Advanced Machine LearningPreprint
Islam, M. S., Orno, A. N., & Arifuzzaman, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4031554/v1 · Cited by 1
Extraction of keywords in the abstract of scientifics papers using BERT
Ikhsan, M., Rachmadi, R. F., & Yuniarno, E. M. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1175/1/012016
Digital Tormenting Identification ON Web-based Entertainment Utilizing Machine Learning
Savanth, S. G., & Geetha, M. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.45358
Cyber bullying Detection on Social Media Using Machine Learning
Gowthami, S., Menaka, S. R., Nilani, K., Priyadharshini, G., Radha Bhuvaneshwari, S., & Roshini, P. (2023) · International Journal of Innovative Research in Engineering · doi:10.59256/ijire.2023040390
Past, Present, and Future of Automatic Cyberbullying Detection Research
Ptaszynski, M. E. (2023) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.35.3_38
A Review on Cyberbullying Detection on Twitter Dataset
Jalote, A., Patan, K. S. K., Mathew, K. N., Nandkar, M., & More, P. (2024) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot59759.2024.10467658
Use of Machine Learning to Identify Fake Profiles
Kumar, G. R. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.59294
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
Amiruzzaman, M., Rahman, A., Farjana, A., & Chowdhury, H. R. (2024) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict62016.2024.10534579 · Cited by 1
Realtime Cyberbullying Detection on Telegram: A Machine Learning Approach with Cyberbot
Barbadekar, A., Raut, S., Laddha, K., & Shekhavat, A. (2024) · IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · doi:10.1109/icbds61829.2024.10837449
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
Devi, K. N., Jayanthi, P., Rajasekar, V., Rathinasamy, R., Dhandapani, P., & Rasil Risam, F. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915097
Wang, L., Wang, T., Zhou, T. H., Jia, X. T., & Zhao, H. L. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033377
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
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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