ALBERT-based fine-tuning model for cyberbullying analysis
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Tripathy et al. used an ALBERT-based fine-tuning model for cyberbullying detection and their proposed method outperformed CNN + word2Vec, CNN + GRU, and BERT implementations with an F1 score of 95%.
“Tripathy et al. [51] used an ALBERT-based fine-tuning model for cyberbullying detection, as it does not require large amounts of data for fine-tuning. The experimental results show that their proposed method outperformed the current approaches CNN + word2Vec, CNN + GRU, and BERT implementations in terms of an F1 Score of 95%.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [51]
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References 23 · Cited by 31
- Authors
- Tripathy, J. K., Chakkaravarthy, S. S., Satapathy, S. C., Sahoo, M., & Vaidehi, V.
- Affiliations
- VIT-AP University (IN)
- KIIT University (IN)
- Mother Teresa Women's University (IN)
- Corresponding author
- Chakkaravarthy, S. Sibi
- ORCID
- Venue
- Multimedia Systems
- Published
- 2020-09-18
- Volume
- 28
- Issue
- 6
- Pages
- 1941-1949
- ISSN
- 0942-4962 (ISSN-L)
- 1432-1882
- OpenAlex
- W3087432510
- Cited by
- 31
- References
- 23
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 8 · 2023: 5 · 2022: 2 · 2021: 2
- FWCI
- 0.88
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- cyberbullying detection, ALBERT, fine-tuning, transformer models, contextual understanding, pre-trained language models, limited training data
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