Uncovering fake likers in online social networks

Prudhvi Ratna Badri Satya, Kyumin Lee, Dongwon Lee, Thanh Tran, Jason Zhang

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

22 Scopus citations

Abstract

As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2365-2370
Number of pages6
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period10/24/1610/28/16

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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