CyberBERT: BERT for cyberbullying identification BERT for cyberbullying identification

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 (similar to 12k posts), Twitter (similar to 16k posts), and Wikipedia (similar to 100k 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.