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Finding unusual review patterns using unexpected rules

Authors:

Nitin Jindal,

Bing Liu,

Ee-Peng LimAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1549 - 1552

https://doi.org/10.1145/1871437.1871669

Published: 26 October 2010 Publication History

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Abstract

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.

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Cited By

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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
Pinter PMorichetta ADustdar S(2024)Distributed Model Serving for Real-time Opinion Detection2024 IEEE International Conference on Service-Oriented System Engineering (SOSE)10.1109/SOSE62363.2024.00014(64-73)Online publication date: 15-Jul-2024
https://doi.org/10.1109/SOSE62363.2024.00014
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Index Terms

Finding unusual review patterns using unexpected rules
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

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
Pinter PMorichetta ADustdar S(2024)Distributed Model Serving for Real-time Opinion Detection2024 IEEE International Conference on Service-Oriented System Engineering (SOSE)10.1109/SOSE62363.2024.00014(64-73)Online publication date: 15-Jul-2024
https://doi.org/10.1109/SOSE62363.2024.00014
Qazi AHasan NMao RElhag Mohamed Abo MKumar Dey SHardaker G(2024)Machine Learning-Based Opinion Spam Detection: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.339926412(143485-143499)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3399264
Pins DWalther MKrüger JStevens GKrauss VAmirkhani S(2024)Digitaler VerbraucherschutzVerbraucherinformatik10.1007/978-3-662-68706-2_4(135-201)Online publication date: 25-Mar-2024
https://doi.org/10.1007/978-3-662-68706-2_4
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
Li SZhong GJin YWu XZhu PWang Z(2023)A Deceptive Reviews Detection Method Based on Multidimensional Feature Construction and Ensemble Feature SelectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314401310:1(153-165)Online publication date: Feb-2023
https://doi.org/10.1109/TCSS.2022.3144013
Li YLiu YLiu C(2023)Research on Spam Review Detection: A Survey2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/ICNC-FSKD59587.2023.10281054(1-6)Online publication date: 29-Jul-2023
https://doi.org/10.1109/ICNC-FSKD59587.2023.10281054
Catelli RBevilacqua LMariniello NDi Carlo VMagaldi MFujita HDe Pietro GEsposito M(2023)A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentimentIEEE Access10.1109/ACCESS.2023.3277490(1-1)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3277490
Duma RNiu ZNyamawe ATchaye-Kondi JYusuf A(2023)A Deep Hybrid Model for fake review detection by jointly leveraging review text, overall ratings, and aspect ratingsSoft Computing10.1007/s00500-023-07897-427:10(6281-6296)Online publication date: 6-Mar-2023
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