Combating Crowdsourced Review Manipulators Conference Paper uri icon


  • © 2018 Association for Computing Machinery. 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.

author list (cited authors)

  • Kaghazgaran, P., Caverlee, J., & Squicciarini, A.

citation count

  • 23

editor list (cited editors)

  • Chang, Y. i., Zhai, C., Liu, Y., & Maarek, Y.

publication date

  • February 2018


  • ACM  Publisher