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- W4285172020 abstract "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 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Overwatch</i> 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." @default.
- W4285172020 created "2022-07-14" @default.
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- W4285172020 date "2022-12-01" @default.
- W4285172020 modified "2023-10-18" @default.
- W4285172020 title "“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features" @default.
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- W4285172020 doi "https://doi.org/10.1109/tg.2022.3176849" @default.
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