Uncovering crowdsourced manipulation of online reviews

Amir Fayazi, Kyumin Lee, James Caverlee, Anna Squicciarini

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

53 Scopus citations

Abstract

Online reviews are a cornerstone of consumer decision making. However, their authenticity and quality has proven hard to control, especially as polluters target these reviews toward promoting products or in degrading competitors. In a troubling direction, the widespread growth of crowdsourcing platforms like Mechanical Turk has created a large-scale, potentially difficult-to-detect workforce of malicious review writers. Hence, this paper tackles the challenge of uncovering crowdsourced manipulation of online reviews through a three-part effort: (i) First, we propose a novel sampling method for identifying products that have been targeted for manipulation and a seed set of deceptive reviewers who have been enlisted through crowdsourcing platforms. (ii) Second, we augment this base set of deceptive reviewers through a reviewer-reviewer graph clustering approach based on a Markov Random Field where we define individual potentials (of single reviewers) and pair potentials (between two reviewers). (iii) Finally, we embed the results of this probabilistic model into a classification framework for detecting crowd-manipulated reviews. We find that the proposed approach achieves up to 0.96 AUC, outperforming both traditional detection methods and a SimRank-based alternative clustering approach.

Original languageEnglish (US)
Title of host publicationSIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages233-242
Number of pages10
ISBN (Electronic)9781450336215
DOIs
StatePublished - Aug 9 2015
Event38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 - Santiago, Chile
Duration: Aug 9 2015Aug 13 2015

Publication series

NameSIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015
CountryChile
CitySantiago
Period8/9/158/13/15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software

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