CyberBERT: 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 ( ∼ 12k posts), Twitter ( ∼ 16k posts), and Wikipedia ( ∼ 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.