mlsanityData to 7 Oct 2026

Cyberbullying detection across social media platforms via platform-aware adversarial encoding

arXiv 2204.00334 ↗First posted 1 Apr 2022cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 1 Apr 2022.

Builds on

11 of 26 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  4. 2017Generative Adversarial Networks: An Overviewcited by 588
  5. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  6. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 285
  7. 2016Ex Machina: Personal Attacks Seen at Scalecited by 208
  8. 2021Towards generalisable hate speech detection: a review on obstacles and solutionscited by 66
  9. 2018Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platformscited by 37
  10. 2018Automatic Detection of Cyberbullying in Social Media Textcited by 25
  11. 2018Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Studycited by 14

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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 3.

6 papers.