Abstract
Toxicity in online gaming is a problem that causes harm to players, developers, and gaming communities. Toxic behaviours persist in online multiplayer games for a number of reasons, and continue to go unchecked due in large part to a lack of reliable methods to accurately detect toxicity online, in real-time, and at scale. In this paper, we present a modeling approach that uses features derived from in-game verbal communication and game metadata to predict if Overwatch games are toxic. With logistic regression models, we achieve accuracy scores of 86.3% for binary (high vs low toxicity) predictions. We discuss which features were most salient, potential application of our predictive model, and implications for toxicity detection in games. Our approach is a low-cost, low-effort, and non-invasive detection approach that contributes to holistic efforts in combating toxicity in games.
Original language | English |
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Pages (from-to) | 558-568 |
Number of pages | 11 |
Journal | IEEE Transactions on Games |
Volume | 14 |
Issue number | 4 |
Early online date | 2022 |
DOIs | |
Publication status | Published - Dec 2022 |
Bibliographical note
Publisher Copyright:IEEE
Keywords
- Feature extraction
- Games
- Overwatch
- Predictive models
- Sensors
- Sports
- Toxicology
- User experience
- classification
- competitive
- esports
- game
- gaming
- multiplayer
- prediction
- reporting
- toxicity