{"type":"ScholarlyArticle","title":"Online Reputation Fraud Campaign Detection in User Ratings","authors":[{"name":"Chang Xu","slug":"chang-xu","given":"Chang","family":"Xu","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Zhu Sun","slug":"zhu-sun","given":"Zhu","family":"Sun","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns are constantly evolving and changing tactics to evade detection.For example, they can launch early attacks on the items to quickly dominate the reputations.They can also whitewash themselves through creating new accounts for subsequent attacks.It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities.In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible.This leads to a unified and scalable optimization framework, FraudScan, that can adapt to emerging fraud patterns over time.Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.","doi":"10.24963/ijcai.2017/541","url":"https://ijcai.org/proceedings/2017/541","pdf":"https://ijcai.org/proceedings/2017/0541.pdf","markdown":"https://ijcai.org/proceedings/2017/541.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/541","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":541,"pages":{"first":3873,"last":3879},"keywords":["Multidisciplinary Topics and Applications: AI&Security and Privacy","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2013] Leman Akoglu, Rishi Chandy, and Christos Faloutsos. 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