Toxicity in online gaming is a problem that causes harm to players, developers, and gaming communities. Toxic behaviors 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 article, 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 noninvasive contribution to holistic efforts in combating toxicity in games.

中文翻译：

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“不良振动”：从游戏内音频功能中感知毒性

在线游戏中的毒性是一个对玩家、开发者和游戏社区造成伤害的问题。由于多种原因，在线多人游戏中的有害行为持续存在，并且继续得不到遏制，这在很大程度上是由于缺乏可靠的方法来实时、大规模地在线准确检测毒性。在本文中，我们提出了一种建模方法，该方法使用源自游戏内口头交流和游戏元数据的特征来预测是否守望先锋游戏是有毒的。使用逻辑回归模型，我们实现了二元（高毒性与低毒性）预测的准确度得分为 86.3%。我们讨论了哪些特征最突出，我们的预测模型的潜在应用，以及对游戏中毒性检测的影响。我们的方法是一种低成本、省力且非侵入性的方法，可以为抗击游戏中的毒性做出全面努力。