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One problem facing players of competitive games is negative, or toxic,behavior. League of Legends, the largest eSport game, uses a crowdsourcingplatform called the Tribunal to judge whether a reported toxic player should bepunished or not. The Tribunal is a two stage system requiring reports fromthose players that directly observe toxic behavior, and human experts thatreview aggregated reports. While this system has successfully dealt with thevague nature of toxic behavior by majority rules based on many votes, itnaturally requires tremendous cost, time, and human efforts.  In this paper, we propose a supervised learning approach for predictingcrowdsourced decisions on toxic behavior with large-scale labeled datacollections; over 10 million user reports involved in 1.46 million toxicplayers and corresponding crowdsourced decisions. Our result shows goodperformance in detecting overwhelmingly majority cases and predictingcrowdsourced decisions on them. We demonstrate good portability of ourclassifier across regions. Finally, we estimate the practical implications ofour approach, potential cost savings and victim protection.

STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online Games