Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGs

Authors:
Muhammad Aurangzeb Ahmad;Brian Keegan;Jaideep Srivastava;Dmitri Williams;Noshir Contractor
Affiliations:
-;-;-;-;-
Venue:
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Year:
2009

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Abstract

Gold farming refers to the illicit practice of gathering and selling virtual goods in online games for real money. Although around one million gold farmers engage in gold farming related activities, to date a systematic study of identifying gold farmers has not been done. In this paper we use data from the massively-multiplayer online role-playing game (MMORPG) EverQuest II to identify gold farmers. We perform an exploratory logistic regression analysis to identify salient descriptive statistics followed by a machine learning binary classification problem to identify a set of features for classification purposes. Given the cost associated with investigating gold farmers, we also give criteria for evaluating gold farming detection techniques, and provide suggestions for future testing and evaluation techniques.