Combating crowdsourced review manipulators: A neighborhood-based approach

Parisa Kaghazgaran, James Caverlee, Anna Squicciarini

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

22 Scopus citations

Abstract

We propose a system called TwoFace to uncover crowdsourced review manipulators who target online review systems. A unique feature of TwoFace is its three-phase framework: (i) in the first phase, we intelligently sample actual evidence of manipulation (e.g., review manipulators) by exploiting low moderation crowdsourcing platforms that reveal evidence of strategic manipulation; (ii) we then propagate the suspiciousness of these seed users to identify similar users through a random walk over a "suspiciousness" graph; and (iii) finally, we uncover (hidden) distant users who serve structurally similar roles by mapping users into a low-dimensional embedding space that captures community structure. Altogether, the TwoFace system recovers 83% to 93% of all manipulators in a sample from Amazon of 38,590 reviewers, even when the system is seeded with only a few samples from malicious crowdsourcing sites.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages306-314
Number of pages9
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

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