Automatic analysis and identification of verbal aggression and abusive behaviors for online social games

• We propose a system to automatically evaluate player complaints in a social game.
• Our database contains 100,000 players, 1000 complaints and 240 abusive players.
• We experiment with several pieces of player information and their combinations.
• Our system can correctly identify abusive players with up to 85% precision.
• We can isolate and identify severe abuse cases with a higher confidence.

Online multiplayer games create new social platforms, with their own etiquette, social rules of conduct and ways of expression. What counts as aggressive and abusing behavior may change depending on the platform, but most online gaming companies need to deal with aggressive and abusive players explicitly. This usually is tied to a reporting mechanism where the offended player reports an offense. In this paper, we develop tools for validating whether a verbal aggression offense report refers to a real offense or not, in the context of a very popular online social game, called Okey. Our approach relies on the analysis of player behavior and characteristics of offending players. In the proposed system, chat records and other social activities in the game are taken into account, as well as player history. This methodology is sufficiently generic, and it can be applied to similar gaming platforms, thus describing a useful tool for game companies. We report our results on data collected over a six months period, involving 100,000 users and 800,000 game records, and illustrate the viability of such analysis, while providing insights on the factors associated with verbal aggression and abusive behavior for social games.

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