Online reputation fraud campaign detection in user ratings

Chang Xu, Jie Zhang, Zhu Sun

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3873-3879
Number of pages7
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

Cite this

Xu, C., Zhang, J., & Sun, Z. (2017). Online reputation fraud campaign detection in user ratings. In C. Sierra (Ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 3873-3879). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/541