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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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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
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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