# Reinforcement Learning for Efficient Toxicity Detection in Competitive Online Video Games

arXiv: https://arxiv.org/abs/2503.20968 · HTML: https://mlsanity.com/paper/2503.20968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 6 of 6)

- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [Automatic Detection of Cyberbullying in Social Media Text](https://mlsanity.com/paper/1801.05617/) (2018)
- [Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games](https://mlsanity.com/paper/1504.02305/) (2015)
- [Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms](https://mlsanity.com/paper/2211.06516/) (2022)
- [The Emotional Impact of Game Duration: A Framework for Understanding Player Emotions in Extended Gameplay Sessions](https://mlsanity.com/paper/2404.00526/) (2024)
- [Uncovering the Effect of Toxicity on Player Engagement and its Propagation in Competitive Online Video Games](https://mlsanity.com/paper/2407.09736/) (2024)

## Abstract (arXiv)

Online platforms take proactive measures to detect and address undesirable behavior, aiming to focus these resource-intensive efforts where such behavior is most prevalent. This article considers the problem of efficient sampling for toxicity detection in competitive online video games. To make optimal monitoring decisions, video game service operators need estimates of the likelihood of toxic behavior. If no model is available for these predictions, one must be estimated in real time. To close this gap, we propose a contextual bandit algorithm that makes monitoring decisions based on a small set of variables that, according to domain expertise, are associated with toxic behavior. This algorithm balances exploration and exploitation to optimize long-term outcomes and is deliberately designed for easy deployment in production. Using data from the popular first-person action game Call of Duty: Modern Warfare III, we show that our algorithm consistently outperforms baseline algorithms that rely solely on players' past behavior. This finding has substantive implications for the nature of toxicity. It also illustrates how domain expertise can be harnessed to help video game service operators identify and mitigate toxicity, ultimately fostering a safer and more enjoyable gaming experience.