  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 modeling and understanding the two defining
characteristics of cyberbullying: repetitive behavior and power imbalance. In
this survey paper, we define the Session-based Cyberbullying Detection
framework that encapsulates the different steps and challenges of the problem.
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 propose
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.
