Generalisation of Cyberbullying Detection
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Abstract (arXiv)
Cyberbullying is a problem in today's ubiquitous online communities. Filtering it out of online conversations has proven a challenge, and efforts have led to the creation of many different datasets, all offered as resources to train classifiers. Through these datasets, we will explore the variety of definitions of cyberbullying behaviors and the impact of these differences on the portability of one classifier to another community. By analyzing the similarities between datasets, we also gain insight on the generalization power of the classifiers trained from them. A study of ensemble models combining these classifiers will help us understand how they interact with each other.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2022Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbationscited by 5
- 2021UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detectioncited by 1
- 2024Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platformscited by 1
- 2022Relationship Between Online Harmful Behaviors and Social Network Message Writing Stylecited by 0
- 2024Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms27 Dec 2024
- 2022Relationship Between Online Harmful Behaviors and Social Network Message Writing Style14 Dec 2022
- 2022Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations17 Jan 2022
- 2021UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection17 Apr 2021