Article

Review spam detection

Authors:

Nitin Jindal,

Bing LiuAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 1189 - 1190

https://doi.org/10.1145/1242572.1242759

Published: 08 May 2007 Publication History

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Abstract

It is now a common practice for e-commerce Web sites to enable their customers to write reviews of products that they have purchased. Such reviews provide valuable sources of information on these products. They are used by potential customers to find opinions of existing users before deciding to purchase a product. They are also used by product manufacturers to identify problems of their products and to find competitive intelligence information about their competitors. Unfortunately, this importance of reviews also gives good incentive for spam, which contains false positive or malicious negative opinions. In this paper, we make an attempt to study review spam and spam detection. To the best of our knowledge, there is still no reported study on this problem.

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Cited By

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Kiran Naik Kajal Naik Devyani More Dipti Kapadi Ms. Poonam Dholi (2024)Spam Review Detection using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17556(361-366)Online publication date: 22-Apr-2024
https://doi.org/10.48175/IJARSCT-17556
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
Sun PBi WZhang YWang QKou FLu TChen J(2024)Fake Review Detection Model Based on Comment Content and Review BehaviorElectronics10.3390/electronics1321432213:21(4322)Online publication date: 4-Nov-2024
https://doi.org/10.3390/electronics13214322
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Index Terms

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 '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

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: 08 May 2007

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WWW'07

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WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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

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Cited By

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Kiran Naik Kajal Naik Devyani More Dipti Kapadi Ms. Poonam Dholi (2024)Spam Review Detection using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17556(361-366)Online publication date: 22-Apr-2024
https://doi.org/10.48175/IJARSCT-17556
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
Sun PBi WZhang YWang QKou FLu TChen J(2024)Fake Review Detection Model Based on Comment Content and Review BehaviorElectronics10.3390/electronics1321432213:21(4322)Online publication date: 4-Nov-2024
https://doi.org/10.3390/electronics13214322
Tabany MGueffal M(2024)Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning ModelJournal of Advances in Information Technology10.12720/jait.15.1.49-5815:1(49-58)Online publication date: 2024
https://doi.org/10.12720/jait.15.1.49-58
Zhao YLi TYuan QDeng S(2024)How to detect fake online physician reviews: A deep learning approachDIGITAL HEALTH10.1177/2055207624127717110Online publication date: 30-Aug-2024
https://doi.org/10.1177/20552076241277171
Lim WAgarwal RMishra AMehrotra A(2024)The Rise of Fake Reviews: Toward a Marketing-Oriented Framework for Understanding Fake ReviewsAustralasian Marketing Journal10.1177/14413582241283505Online publication date: 28-Oct-2024
https://doi.org/10.1177/14413582241283505
Van Dinh CLuu S(2024)Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce WebsitesInternational Journal of Asian Language Processing10.1142/S2717554524500024Online publication date: 29-Jul-2024
https://doi.org/10.1142/S2717554524500024
Chen KZhou ZLi YJi XWu JCoatrieux JChen YCoatrieux G(2024)RED-Net: Residual and Enhanced Discriminative Network for Image Steganalysis in the Internet of Medical Things and TelemedicineIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.331646828:3(1611-1622)Online publication date: Mar-2024
https://doi.org/10.1109/JBHI.2023.3316468
K KS SG SP SA S(2024)Deep Inception V5 Convolution Neural Network to detect and prevent the propagation of deepfake information in Social Media Applications and Research Databases2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714749(787-791)Online publication date: 3-Oct-2024
https://doi.org/10.1109/I-SMAC61858.2024.10714749
Abd-Alhalem SAli HSoliman NAlgarni AMarie H(2024)Advancing E-Commerce Authenticity: A Novel Fusion Approach Based on Deep Learning and Aspect Features for Detecting False ReviewsIEEE Access10.1109/ACCESS.2024.343591612(116055-116070)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3435916
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