Session-based cyberbullying detection in social media: A survey
Open accessAbstract
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.
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A shortage of labeled social media platform datasets suitable for training effective cyberbullying detection systems exists.
“This leads to a shortage of labeled social media platform datasets suitable for training effective cyberbullying detection systems [150].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [150]
Models like BERT and RoBERTa excel in natural language processing tasks but have not been broadly utilized for cyberbullying detection.
“While models like BERT and RoBERTa excel in natural language processing tasks, they haven't been broadly utilized for cyberbullying detection [150].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [150]
In cyberbullying detection, the lack of a clear understanding of how models make predictions presents a significant challenge.
“In cyberbullying detection, the lack of a clear understanding of how models make predictions presents a significant challenge [150].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [150]
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Learning like human annotators: Cyberbullying detection in lengthy social media sessions
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Image cyberbullying detection and recognition using transfer deep machine learning
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- Authors
- Yi, P., & Zubiaga, A.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Yi, Peiling
- Venue
- Online Social Networks and Media
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 36
- Pages
- 100250
- ISSN
- 2468-6964 (ISSN-L)
- OpenAlex
- W4381094526
- Cited by
- 55
- References
- 132
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 24 · 2023: 6
- FWCI
- 5.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Online Social Networks and Media (publishedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- cyberbullying detection, social media, repetitive behavior, power imbalance, benchmarking, large language models, online abuse
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