Session-based cyberbullying detection in social media
A survey
Bibliographic Data
| ID | 22156919 |
|---|---|
| Authors | Peiling Yi (0000-0001-6680-6320, Queen Mary University of London, corresponding author), Arkaitz Zubiaga (0000-0003-4583-3623, Queen Mary University of London) |
| Year | 2023 |
| Volume | 36 |
| Pages | 100250 |
| Publication date | 2023-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Online Social Networks and Media (JOURNAL) |
| Journal identifiers | ISSN: 2468-6964 • E-ISSN: 2468-6964 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.osnem.2023.100250 |
| OpenAlex | W4381094526 |
| Language | EN |
| Citations received | 3 |
| References cited | 43 |
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
Data science · Internet privacy · Open research · Social media · World Wide Web · Bullying, Victimization, and Aggression · Computer Science · Hate Speech and Cyberbullying Detection · Stalking, Cyberstalking, and Harassment · Artificial Intelligence
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1.5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |