The ones that got away: False negative estimation based approaches for gold farmer detection

The problem of gold farmer detection is the problem of detecting players with illicit behaviors in massively multiplayer online games (MMOs) and has been studied extensively. Detecting gold farmers or other deviant actors in social systems is traditionally understood as a binary classification problem, but the issue of false negatives is significant for administrators as residual actors can serve as the backbone for subsequent clandestine organizing. In this paper we address this gap in the literature by addressing the problem of false negative estimation for gold farmers in MMOs by employing the capture-recapture technique for false negative estimation and combine it with graph clustering techniques to determine 'hidden' gold farmers in social networks of farmers and normal players. This paper redefines the problem of gold farming as a false negative estimation problem and estimates the gold farmers in co-extensive MMO networks, previously undetected by the game administrators. It also identifies these undetected gold farmers using graph partitioning techniques and applies network data to address rare class classification problem. The experiments in this research found 53% gold farmers who were previously undetected by the game administrators.