Session-based Cyberbullying Detection in Social Media: A Survey

Peiling Yi, Arkaitz Zubiaga

Online Social Networks and Media. 2023.

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four different steps session-based cyberbullying detection should go through, and discuss the multiple challenges that differ from single text-based cyberbullying detection. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to proposing evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.

@article{yi2023session,
  title={Session-based cyberbullying detection in social media: A survey},
  author={Yi, Peiling and Zubiaga, Arkaitz},
  journal={Online Social Networks and Media},
  volume={36},
  pages={100250},
  year={2023},
  publisher={Elsevier}
}