research-article

Opinion spam and analysis

Authors:

Bing LiuAuthors Info & Claims

WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

Pages 219 - 230

https://doi.org/10.1145/1341531.1341560

Published: 11 February 2008 Publication History

Abstract

Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them

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Cited By

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
Obot OAttai KOnwodi GJames IJohn A(2024)Sentiment Analysis of Electronic Word of Mouth (E-WoM) on E-LearningEncyclopedia of Information Science and Technology, Sixth Edition10.4018/978-1-6684-7366-5.ch057(1-23)Online publication date: 1-Jul-2024
https://doi.org/10.4018/978-1-6684-7366-5.ch057
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
Show More Cited By

Index Terms

Opinion spam and analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

February 2008

270 pages

ISBN:9781595939272

DOI:10.1145/1341531

General Chair:
Marc Najork
Microsoft, USA
,
Program Chairs:
Andrei Broder
Yahoo!, USA
,
Soumen Chakrabarti
IIT Bombay, India

Copyright © 2008 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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Publication History

Published: 11 February 2008

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Cited By

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
Obot OAttai KOnwodi GJames IJohn A(2024)Sentiment Analysis of Electronic Word of Mouth (E-WoM) on E-LearningEncyclopedia of Information Science and Technology, Sixth Edition10.4018/978-1-6684-7366-5.ch057(1-23)Online publication date: 1-Jul-2024
https://doi.org/10.4018/978-1-6684-7366-5.ch057
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
Hsieh YLu LYang R(2024)Trustworthiness of Review Opinions on the Internet for 3C CommoditiesElectronics10.3390/electronics1307134613:7(1346)Online publication date: 3-Apr-2024
https://doi.org/10.3390/electronics13071346
Wani MElAffendi MShakil K(2024)AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language ProcessingComputers10.3390/computers1310026413:10(264)Online publication date: 12-Oct-2024
https://doi.org/10.3390/computers13100264
Kim CKim H(2024)Efficient Detection of Irrelevant User Reviews Using Machine LearningApplied Sciences10.3390/app1416690014:16(6900)Online publication date: 7-Aug-2024
https://doi.org/10.3390/app14166900
Harris C(2024)Exploring Transformer Models and Domain Adaptation for Detecting Opinion Spam in Reviews2024 36th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT64283.2024.10749897(249-255)Online publication date: 30-Oct-2024
https://doi.org/10.23919/FRUCT64283.2024.10749897
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
Karamanolakis GHsu DGravaano L(2024)Interactive Machine Teaching by Labeling Rules and InstancesTransactions of the Association for Computational Linguistics10.1162/tacl_a_0070712(1441-1459)Online publication date: 18-Nov-2024
https://doi.org/10.1162/tacl_a_00707
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
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