# Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

arXiv: https://arxiv.org/abs/1504.02305 · HTML: https://mlsanity.com/paper/1504.02305/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2015.

## Built on by (most cited first, 10 of 10)

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- [CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System](https://mlsanity.com/paper/2406.19720/) (2024)
- [Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety](https://mlsanity.com/paper/2303.15110/) (2023)
- [Reinforcement Learning for Efficient Toxicity Detection in Competitive Online Video Games](https://mlsanity.com/paper/2503.20968/) (2025)
- [Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata](https://mlsanity.com/paper/2504.01534/) (2025)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)
- [PSK@EEUCA 2026: Fine-Tuning Large Language Models with Synthetic Data Augmentation for Multi-Class Toxicity Detection in Gaming Chat](https://mlsanity.com/paper/2605.07201/) (2026)

## Abstract (arXiv)

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.