poster

Towards online review spam detection

Authors:

Yuming Lin,

Tao Zhu,

Xiaoling Wang,

Jingwei Zhang,

Aoying ZhouAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 341 - 342

https://doi.org/10.1145/2567948.2577293

Published: 07 April 2014 Publication History

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Abstract

User reviews play a crucial role in Web, since many decisions are made based on them. However, review spam would misled the users, which is extremely obnoxious. In this poster, we explore the problem of online review spam detection. Firstly, we devise six features to find the spam based on the review content and reviewer behaviors. Secondly, we apply supervised methods and an unsupervised one for spotting the review spam as early as possible. Finally, we carry out intensive experiments on a real-world review set to verify the proposed methods.

References

[1]

N. Jindal and B. Liu. Analyzing and detecting review spam. In ICDM 2007, pages 547--552. IEEE, 2007.

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[2]

C. Lai, K. Xu, R. Y. Lau, and L.Jing. Toward a language modeling approach for consumer review spam detection. In ICBE 2010, pages 241--244. ACM, 2010.

Crossref

Google Scholar

[3]

M. Ott, Y. Choi, C. Cardie, and J. T. Hancock. Finding deceptive opinion spam by any stretch of the imagination. arXiv preprint arXiv:1107.4557, 2011.

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[4]

G. Wu, D. Greene, and P. Cunningham. Merging multiple criteria to identify suspicious reviews. In RecSys 2010, pages 241--244. ACM, 2010.

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Cited By

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Zhang YWang HStavrou A(2024)A multiview clustering framework for detecting deceptive reviewsJournal of Computer Security10.3233/JCS-22000132:1(31-52)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.3233/JCS-220001
Guarasci RCatelli REsposito M(2024)Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian languageExpert Systems with Applications10.1016/j.eswa.2024.124131252(124131)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124131
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
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Index Terms

Towards online review spam detection
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

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Author Tags

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Poster

Funding Sources

Ministry of Science and Technology of the People's Republic of China
Natural Science Foundation of Guangxi Province
National Natural Science Foundation of China
Guangxi Provincial Department of Education

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Zhang YWang HStavrou A(2024)A multiview clustering framework for detecting deceptive reviewsJournal of Computer Security10.3233/JCS-22000132:1(31-52)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.3233/JCS-220001
Guarasci RCatelli REsposito M(2024)Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian languageExpert Systems with Applications10.1016/j.eswa.2024.124131252(124131)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124131
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
Rani MSumathy S(2022)A Study on Diverse Methods and Performance Measures in Sentiment AnalysisRecent Patents on Engineering10.2174/187221211499920101915495416:3Online publication date: May-2022
https://doi.org/10.2174/1872212114999201019154954
Wang LWang HLuo XZhang TWang SLiu XRoychoudhury ACadar CKim M(2022)Demystifying “removed reviews” in iOS app storeProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3558966(1489-1499)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3558966
Agarwal RSharma D(2022)Detecting Fake Reviews using Machine learning techniques: a survey2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE53722.2022.9823633(1750-1756)Online publication date: 28-Apr-2022
https://doi.org/10.1109/ICACITE53722.2022.9823633
Abdulqader MNamoun AAlsaawy Y(2022)Fake Online Reviews: A Unified Detection Model Using Deception TheoriesIEEE Access10.1109/ACCESS.2022.322763110(128622-128655)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3227631
Vidanagama DSilva AKarunananda A(2022)Ontology based sentiment analysis for fake review detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117869206:COnline publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117869
Zhong MLi ZLiu SYang BTan RQu X(2021)Fast Detection of Deceptive Reviews by Combining the Time Series and Machine LearningComplexity10.1155/2021/99233742021(1-11)Online publication date: 29-May-2021
https://doi.org/10.1155/2021/9923374
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