Session-based Cyberbullying Detection in Social Media: A Survey

• URL: http://arxiv.org/abs/2207.10639v1
• Date: Thu, 14 Jul 2022 18:56:54 GMT
• Title: Session-based Cyberbullying Detection in Social Media: A Survey
• Authors: Peiling Yi and Arkaitz Zubiaga
• Abstract summary: We define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Our review leads us to propose evidence-based criteria for a set of best practices to create session-based cyberbullying datasets.
• Score: 16.39344929765961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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