“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features

Elizabeth Reid, Regan L. Mandryk, Nicole A. Beres, Madison Klarkowski, J. Frommel

Research output: Contribution to journalArticleAcademicpeer-review

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 languageEnglish
Pages (from-to)558-568
Number of pages11
JournalIEEE Transactions on Games
Volume14
Issue number4
Early online date2022
DOIs
Publication statusPublished - 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

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