Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models
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If pre-trained models do not align well with the specific requirements of cyberbullying detection, performance might suffer.
“Should the pre-trained models not align well with the specific requirements of cyberbullying detection, the performance might suffer [159].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [159]
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- Authors
- Yafooz, W. M. S., Al-Dhaqm, A., & Alsaeedi, A.
- Affiliations
- Taibah University (SA)
- Corresponding author
- Yafooz, Wael M. S.
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2023
- Pages
- 255-267
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W4321442855
- Cited by
- 17
- References
- 36
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 8
- FWCI
- 9.41
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Studies in computational intelligence (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, children, transfer learning, transformer fine-tuning, BERT, social media comments, digital platforms, inappropriate content, text classification
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