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Multimedia Systems

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11-11-2020 | Special Issue Paper

CyberBERT: BERT for cyberbullying identification

BERT for cyberbullying identification

Authors: Sayanta Paul, Sriparna Saha

Published in: Multimedia Systems | Issue 6/2022

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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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Title: CyberBERT: BERT for cyberbullying identification
BERT for cyberbullying identification
Authors: Sayanta Paul
Sriparna Saha
Publication date: 11-11-2020
Publisher: Springer Berlin Heidelberg
Published in: Multimedia Systems / Issue 6/2022
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-020-00710-4

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