“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features
“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features
复制标题
“不良振动”：从游戏中的音频功能中感知毒性
DOI：
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发表时间：
2022
影响因子：
2.3
通讯作者：
Julian Frommel
中科院分区：
文献类型：
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作者：
Elizabeth Reid;R. Mandryk;Nicole A. Beres;Madison Klarkowski;Julian Frommel
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.
使用语言和社交特征预测 Instagram 上敌意的存在和强度
DOI：
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发表时间：
2018
期刊：
Proceedings of the 12th International Conference on Web and Social Media
影响因子：
--
作者：
Liu, P.;Guberman, J.;Hemphill, L.;Culotta, A.
通讯作者：
Culotta, A.