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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ahmad, M.A. Keegan, B. Roy, A. Williams, D. Srivastava, J. Contractor, N. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Comput. Sci., Univ. of Minnesota, Minneapolis, MN, USA (Ahmad, M.A.; Roy, A.; Srivastava, J.) || Sch. of Commun., Northwestern Univ., Evanston, IL, USA (Contractor, N.) || Dept. of Political Sci., Northeastern Univ., Boston, MA, USA (Keegan, B.) || Annenberg Sch. of Commun., Univ. of Southern California, Los Angeles, CA, USA (Williams, D.) |
| Abstract | The term `Gold Farmer' refers to a class of players in massive online games (MOGs) involved in a set of inter-related 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. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 121 |
| Ending Page | 126 |
| File Size | 501842 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450322409 |
| DOI | 10.1145/2492517.2492649 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-08-25 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | ACM and IEEE |
| Subject Keyword | Employee welfare Gold Social network services Conferences Games Educational institutions Electronic mail |
| Content Type | Text |
| Resource Type | Article |
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