This research shows that observing in-game behavior can support the work of community managers in moderating and possibly containing the burden of toxic behavior.Īs online social platforms increasingly impact society, scholars and activists have criticized the centralized and unaccountable nature of many of their governance structures.

In particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. banned) and the sanction type (offensive behavior vs. Our findings, based on supervised learning with random forests, suggest that it is not only possible to behaviorally distinguish sanctioned from unsanctioned players based on selected features of gameplay it is also possible to predict both the sanction severity (warned vs. unfair advantage) and degree of severity (warned vs. Sanctioned players are defined by their toxic action type (offensive behavior vs. We test our hypothesis of detecting toxicity through gameplay with a dataset of almost 1,800 sanctioned players, and comparing these sanctioned players with unsanctioned players. players that have been sanctioned by Ubisoft community managers). Is it possible to detect toxicity in games just by observing in-game behavior? If so, what are the behavioral factors that will help machine learning to discover the unknown relationship between gameplay and toxic behavior? In this initial study, we examine whether it is possible to predict toxicity in the MOBA gameFor Honor by observing in-game behavior for players that have been labeled as toxic (i.e.

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