Guilt by association?: network based propagation approaches for gold farmer detection

Authors:
Muhammad Aurangzeb Ahmad;Brian Keegan;Atanu Roy;Dmitri Williams;Jaideep Srivastava;Noshir Contractor
Affiliations:
University of Minnesota;Northeastern University;University of Minnesota;University of Southern California;University of Minnesota;Northwestern University
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

The term 'Gold Farmer' refers to a class of players in massive online games (MOGs) involved in a set of interrelated activities which are considered to be deviant activities. Consequently these gold farmers are actively banned by game administrators. The task of gold farmer detection is to identify gold farmers in a population of players but just like other clandestine actors they not labeled as such. In this paper the problem of extending the label of gold farmers to players which are not labeled as such is considered. Two main classes of techniques are described and evaluated: Network-based approaches and similarity based approaches. It is also explored how dividing the problem further by relabeling the data based on behavioral patterns can further improve the results