Cyberbullying Detection across Social Media Platforms via Platform-Aware Adversarial Encoding
DOI:
https://doi.org/10.1609/icwsm.v16i1.19401Keywords:
Qualitative and quantitative studies of social media, Text categorization; topic recognition; demographic/gender/age identificationAbstract
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.Downloads
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
Issue
Section
Poster Papers