Cyberbullying Detection across Social Media Platforms via Platform-Aware Adversarial Encoding

Authors

  • Peiling Yi Queen Mary University of London
  • Arkaitz Zubiaga Queen Mary University of London

DOI:

https://doi.org/10.1609/icwsm.v16i1.19401

Keywords:

Qualitative and quantitative studies of social media, Text categorization; topic recognition; demographic/gender/age identification

Abstract

Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms has received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.

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Published

2022-05-31

How to Cite

Yi, P., & Zubiaga, A. (2022). Cyberbullying Detection across Social Media Platforms via Platform-Aware Adversarial Encoding. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 1430-1434. https://doi.org/10.1609/icwsm.v16i1.19401