Automatic Classification of Player Complaints in Social Games

Artificial intelligence and machine learning techniques are not only useful for creating plausible behaviors for interactive game elements, but also for the analysis of the players to provide a better gaming environment. In this paper, we propose a novel framework for automatic classification of player complaints in a social gaming platform. We use features that describe both parties of the complaint (namely, the accuser and the suspect), as well as interaction features of the game itself. The proposed classification approach, based on gradient boosting machines, is tested on the COPA Database of 100 000 unique users and 800 000 individual games. We advance the state of the art in this challenging problem.