# Generalisation of Cyberbullying Detection

arXiv: https://arxiv.org/abs/2009.01046 · HTML: https://mlsanity.com/paper/2009.01046/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations](https://mlsanity.com/paper/2201.06384/) (2022)
- [UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection](https://mlsanity.com/paper/2104.08635/) (2021)
- [Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms](https://mlsanity.com/paper/2412.19928/) (2024)
- [Relationship Between Online Harmful Behaviors and Social Network Message Writing Style](https://mlsanity.com/paper/2212.07526/) (2022)

## 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.