research-article

Simultaneously detecting fake reviews and review spammers using factor graph model

Authors:

Yangguang LiAuthors Info & Claims

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

Pages 225 - 233

https://doi.org/10.1145/2464464.2464470

Published: 02 May 2013 Publication History

Abstract

Review spamming is quite common on many online shopping platforms like Amazon. Previous attempts for fake review and spammer detection use features of reviewer behavior, rating, and review content. However, to the best of our knowledge, there is no work capable of detecting fake reviews and review spammers at the same time. In this paper, we propose an algorithm to achieve the two goals simultaneously. By defining features to describe each review and reviewer, a Review Factor Graph model is proposed to incorporate all the features and to leverage belief propagation between reviews and reviewers. Experimental results show that our algorithm outperforms all of the other baseline methods significantly with respect to both efficiency and accuracy.

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Cited By

Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Ashraf SJaved ABellary SBala PPanigrahi P(2024)Leveraging Stacking Framework for Fake Review Detection in the Hospitality SectorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1902007519:2(1517-1558)Online publication date: 15-Jun-2024
https://doi.org/10.3390/jtaer19020075
Rout JSahoo KDalmia ABakshi SBilal MSong H(2024)Understanding Large-Scale Network Effects in Detecting Review SpammersIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324313911:4(4994-5004)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3243139
Show More Cited By

Index Terms

Simultaneously detecting fake reviews and review spammers using factor graph model
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

May 2013

481 pages

ISBN:9781450318891

DOI:10.1145/2464464

Conference Chairs:
Hugh Davis
University of Southampton
,
Harry Halpin
World Wide Web Consortium
,
Alex Pentland,
Program Chairs:
Mark Bernstein,
Lada Adamic,
Harith Alani,
Alexandre Monnin,
Richard Rogers

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2013

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Shenzhen Key Laboratories

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WebSci '13: Web Science 2013

May 2 - 4, 2013

Paris, France

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Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Ashraf SJaved ABellary SBala PPanigrahi P(2024)Leveraging Stacking Framework for Fake Review Detection in the Hospitality SectorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1902007519:2(1517-1558)Online publication date: 15-Jun-2024
https://doi.org/10.3390/jtaer19020075
Rout JSahoo KDalmia ABakshi SBilal MSong H(2024)Understanding Large-Scale Network Effects in Detecting Review SpammersIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324313911:4(4994-5004)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3243139
Abedin EMendoza AAkbarighatar PKarunasekera S(2024)Predicting Credibility of Online Reviews: An Integrated ApproachIEEE Access10.1109/ACCESS.2024.338384612(49050-49061)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383846
Wang RChen H(2023)Detecting Inactive Cyberwarriors from Online Forums2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00008(9-15)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00008
Rout JDalmia ARath SMohanta BRamasubbareddy SGandomi A(2023)Detecting Product Review Spammers Using Principles of Big DataIEEE Transactions on Engineering Management10.1109/TEM.2021.309780570:7(2516-2527)Online publication date: Jul-2023
https://doi.org/10.1109/TEM.2021.3097805
Ben Jabeur SBallouk HBen Arfi WSahut J(2023)Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for researchJournal of Business Research10.1016/j.jbusres.2022.113631158(113631)Online publication date: Mar-2023
https://doi.org/10.1016/j.jbusres.2022.113631
Cai MDu YTan YLu X(2023)Aspect-based classification method for review spam detectionMultimedia Tools and Applications10.1007/s11042-023-16293-x83:7(20931-20952)Online publication date: 5-Aug-2023
https://doi.org/10.1007/s11042-023-16293-x
Sansonetti GGasparetti FMicarelli A(2023)A Machine Learning Approach to Prediction of Online Reviews ReliabilitySocial Computing and Social Media10.1007/978-3-031-35915-6_11(131-145)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35915-6_11
Al-Zoubi AMora AFaris H(2022)Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature AnalysisApplied Sciences10.3390/app1207363412:7(3634)Online publication date: 3-Apr-2022
https://doi.org/10.3390/app12073634
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