Abstract
Cyberbullying can be delineated as a purposive and recurrent act, which is aggressive in nature, done via different social media platforms such as Facebook, Twitter, Instagram, and others. A state-of-the-art pre-training language model, BERT (Bidirectional Encoder Representations from Transformers), has achieved remarkable results in many language understanding tasks. In this paper, we present a novel application of BERT for cyberbullying identification. A straightforward classification model using BERT is able to achieve the state-of-the-art results across three real-world corpora: Formspring (\(\sim 12\hbox {k}\) posts), Twitter (\(\sim 16\hbox {k}\) posts), and Wikipedia (\(\sim 100\hbox {k}\) posts). Experimental results demonstrate that our proposed model achieves significant improvements over existing works, in comparison with the slot-gated or attention-based deep neural network models.
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Acknowledgements
Dr. Sriparna Saha gratefully acknowledges the Young Faculty Research Fellowship (YFRF) Award, supported by Visvesvaraya Ph.D. Scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia) for carrying out this research.
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Paul, S., Saha, S. CyberBERT: BERT for cyberbullying identification. Multimedia Systems 28, 1897–1904 (2022). https://doi.org/10.1007/s00530-020-00710-4
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DOI: https://doi.org/10.1007/s00530-020-00710-4