research-article

Detecting reviewer bias through web-based association mining

Authors:

Jessica Staddon,

Richard ChowAuthors Info & Claims

WICOW '08: Proceedings of the 2nd ACM workshop on Information credibility on the web

Pages 5 - 10

https://doi.org/10.1145/1458527.1458532

Published: 30 October 2008 Publication History

Abstract

Online retailers and content distributors benefit from an active community that shares credible reviews and recommendations. Today, the most popular approach to encouraging credibility in these communities is self-regulation; community members rate reviews according to their accuracy and usefulness, thus helping to weed out reviews that are inaccurate. This self-regulation, while powerful, is limited by its insularity. Community members generally base their assessments on a reviewer's comments and actions only within the community. This ignores relationships the reviewer has outside the community that may be quite relevant to evaluating the reviewer's comments; for example, a relationship between an author and reviewer. We present a simple method for mining the Web to detect many such associations. Our method, together with self-regulation, provides for more comprehensive detection of bias in reviews by alerting the user to the potential for an undisclosed relationship between a reviewer and author. We provide preliminary results using book reviews in Amazon.com demonstrating that our approach is a high-precision method for detecting strong relationships between reviewers and authors that may contribute to reviewer bias.

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Cited By

Khasawneh M(2022)Enhancing Smart System PlatformsInternational Journal of Technology and Human Interaction10.4018/IJTHI.29320218:1(1-14)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJTHI.293202
Al-Bashayreh MAlmajali DAltamimi AMasa’deh RAl-Okaily M(2022)An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps UsageSustainability10.3390/su1407432514:7(4325)Online publication date: 6-Apr-2022
https://doi.org/10.3390/su14074325
Sikora RChauhan K(2012)Estimating sequential bias in online reviewsKnowledge-Based Systems10.1016/j.knosys.2011.10.01127(314-321)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1016/j.knosys.2011.10.011
Show More Cited By

Index Terms

Detecting reviewer bias through web-based association mining
1. Security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

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Published In

cover image ACM Conferences

WICOW '08: Proceedings of the 2nd ACM workshop on Information credibility on the web

October 2008

100 pages

ISBN:9781605582597

DOI:10.1145/1458527

General Chairs:
Katsumi Tanaka
Kyoto University, Japan
,
Takashi Matsuyama
Kyoto University, Japan
,
Ee-Peng Lim
Singapore Management University, Singapore
,
Program Chair:
Adam Jatowt
Kyoto University, Japan

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2008

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CIKM08

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CIKM08: Conference on Information and Knowledge Management

October 30, 2008

California, Napa Valley, USA

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Overall Acceptance Rate 9 of 19 submissions, 47%

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Cited By

Khasawneh M(2022)Enhancing Smart System PlatformsInternational Journal of Technology and Human Interaction10.4018/IJTHI.29320218:1(1-14)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJTHI.293202
Al-Bashayreh MAlmajali DAltamimi AMasa’deh RAl-Okaily M(2022)An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps UsageSustainability10.3390/su1407432514:7(4325)Online publication date: 6-Apr-2022
https://doi.org/10.3390/su14074325
Sikora RChauhan K(2012)Estimating sequential bias in online reviewsKnowledge-Based Systems10.1016/j.knosys.2011.10.01127(314-321)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1016/j.knosys.2011.10.011
Wu GGreene DSmyth BCunningham PMelville PLeskovec JProvost F(2010)Distortion as a validation criterion in the identification of suspicious reviewsProceedings of the First Workshop on Social Media Analytics10.1145/1964858.1964860(10-13)Online publication date: 25-Jul-2010
https://dl.acm.org/doi/10.1145/1964858.1964860

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