The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
CyberBERT: BERT for cyberbullying identification
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 [22]
In the citing paper
“In addition, understanding the context of the text or images increases the chance of providing better accuracy [19-23].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]
In the citing paper
“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 [22]
In the citing paper
“BERT was applied to three real-world datasets: Formspring, Twitter, and Wikipedia by Paul et al. [22]. According to experimental findings, BERT performs significantly better than conventional machine learning algorithms such as CNN, RNN + LSTM, and Bi-LSTM, with attention in terms of F1 scores.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]
In the citing paper
“BERT can accurately detect bullying behavior in social media messages when the cue is subtle or indirect [22,116,117].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]
In the citing paper
“BERT (Bidirectional Encoder Representations from Transformers) has demonstrated outstanding results in detecting cyberbullying since it is an extensive language model that can extract contextual information from a given text.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]
In the citing paper
“They discovered that their model outperforms prior studies when compared to slot-gated or attention-based Deep Neural Network models.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]
Citations
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
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840
Cyberbullying: Its nature and impact in secondary school pupils
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01846.x · Cited by 3,639
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
Scientific method: Statistical errors
Nuzzo, R. (2014) · Nature · doi:10.1038/506150a · Cited by 1,536
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
Hate Speech Detection with Comment Embeddings
Djuric, N., Zhou, J. L., Morris, R., Grbovic, M., Radosavljević, V., & Bhamidipati, N. L. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742760 · Cited by 725
Ex Machina: Personal Attacks Seen at Scale
Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636
Modeling the Detection of Textual Cyberbullying
Dinakar, K., Reichart, R., & Lieberman, H. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i3.14209 · Cited by 519
Using Machine Learning to Detect Cyberbullying
Reynolds, K., Kontostathis, A., & Edwards, L. (2011) · doi:10.1109/icmla.2011.152 · Cited by 462
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-36687-2_77 · Cited by 396
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
Agrawal, S., & Awekar, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-76941-7_11 · Cited by 387
Improving cyberbullying detection using Twitter users’ psychological features and machine learning
Balakrishnan, V., Khan, S., & Arabnia, H. R. (2020) · Computers & Security · doi:10.1016/j.cose.2019.101710 · Cited by 257
Identification and characterization of cyberbullying dynamics in an online social network
Squicciarini, A., Rajtmajer, S. M., Liu, Y., & Griffin, C. H. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809398 · Cited by 94
Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model
Aggarwal, A., Chauhan, A., Kumar, D., Mittal, M., & Verma, S. (2018) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.13-7-2018.163973 · Cited by 54
Pavlopoulos, J., Thain, N., Dixon, L., & Androutsopoulos, I. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2102 · Cited by 52
Cyberbullying Identification Using Participant-Vocabulary Consistency
Raisi, E., & Huang, B. (2016) · arXiv · doi:10.48550/arxiv.1606.08084 · Cited by 38
Flagged first, then by citations
Research works printed but not linked: 2
Cited by
Sorted by citations
Open-world Machine Learning: Applications, Challenges, and Opportunities
Parmar, J., Chouhan, S. S., Raychoudhury, V., & Rathore, S. S. (2022) · ACM Computing Surveys · doi:10.1145/3561381 · Cited by 163
Murshed, B. A. H., Abawajy, J., Mallappa, S., Saif, M. A. N., & Al-Ariki, H. D. E. (2022) · IEEE Access · doi:10.1109/access.2022.3153675 · Cited by 137
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
“…Twitter [2,63] Time-series Social media content [6,7,21,68,134], Visual contents [6], Wikipedia, Twitter, Formspring and YouTube [25], CyberBERT [22], Bangla text [18], Indonesian language [64], Text and emoji data [135], Facebook [136], Twitter[2,136] Text classification, speech recognition,…”
BackgroundMethodResultInfluentialA Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media
Kumar, A., & Sachdeva, N. (2022) · World Wide Web · doi:10.1007/s11280-021-00920-4 · Cited by 87
Teng, T. H., & Varathan, K. D. (2023) · IEEE Access · doi:10.1109/access.2023.3275130 · Cited by 77
Metadata
- Authors
- Paul, S., & Saha, S.
- Affiliations
- Indian Institute of Technology Patna (IN)
- Corresponding author
- Saha, Sriparna
- ORCID
- Saha, Sriparna
- Venue
- Multimedia Systems
- Published
- 2020-11-11
- Volume
- 28
- Issue
- 6
- Pages
- 1897-1904
- ISSN
- 0942-4962 (ISSN-L)
- 1432-1882
- OpenAlex
- W3098614582
- Cited by
- 116
- References
- 19
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 23 · 2023: 34 · 2022: 14 · 2021: 8
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Systems (publishedVersion)
- 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 › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, BERT, pre-trained language models, text classification, social media posts, Twitter corpus, Wikipedia corpus, online aggression
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 ›
Are you an author, or do you see a problem with this page? Request a review
