The identification of deviance and its impact on retention in a multiplayer game

Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, toxicity index, to identify deviant players. We then look at the effects of interacting with deviant players, including effects on retention. We find that toxic players have several significant predictive patterns, such as playing in more competitive game modes and playing with friends. We also show that toxic players drive away new players, but that experienced players are more resilient to deviant behavior. Based on our findings, we suggest methods to better identify and counteract the negative effects of deviance.

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