Cyberbullying detection across social media platforms via platform-aware adversarial encoding
This addresses the need for more robust cyberbullying detection tools that work across multiple platforms, though it is incremental as it builds on existing Transformer and adversarial learning methods.
The paper tackled the problem of limited generalizability in cyberbullying detection across different social media platforms by proposing XP-CB, a cross-platform framework using Transformers and adversarial learning, which showed effectiveness in experiments across three platforms with BERT and RoBERTa models.
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 have 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.