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.

References

[1]

B. Adler and L. de Alfaro. A content-driven reputation system for the wikipedia. WWW 2007.

Digital Library

[2]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994.

Digital Library

[3]

R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A. Verkamo. Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Cambridge, MA.

Digital Library

[4]

D. Bollegala, Y. Matsuo and M. Ishizuka. Measuring semanticsimilarity between words using Web search engines. WWW 2007.

Digital Library

[5]

J. Chevalier and D. Mayzlin. The Effect of Word of Mouth on Sales: Online Book Reviews. August 6, 2003. Yale SOM Working Paper No's. ES-28 & MK-15.

[6]

R. Chow, P. Golle and J. Staddon. Detecting privacy leaks using corpus-based association rules. In ACM SIGKDD 2008.

Digital Library

[7]

P. Cimiano and S. Staab. Learning by Googling, ACM SIGKDD Explorations Newsletter, 2004.

Digital Library

[8]

comScore and The Kelsey Group. November 29,2007.

[9]

M. Dowman, V. Tablan, H. Cunningham and B. Popov. Web-Assisted Annotation, Semantic Indexing and Search of Television and Radio News. WWW 2005.

Digital Library

[10]

eModeration. http://www.emoderation.com

[11]

N. Glance. Community search assistant. The 6th International Conference on Intelligent User Interfaces, 2001.

Digital Library

[12]

R. Guha, R. Kumar, P. Raghavan and A. Tomkins. Propagation of trust and distrust. WWW 2004.

Digital Library

[13]

H. Lauw, E-P.Lim and K. Wang. Bias and controversy:beyond statistical deviation. KDD 2006.

Digital Library

[14]

V. Griffith. WikiScanner. http://virgil.gr

[15]

A. Gome. Web two point uh-oh! "It's my shout" Blog on Smart Company. March, 2007.

[16]

Hakia. http://www.hakia.com/

[17]

V. Mickunas. Amazon.com book reviewer shakeout. Dayton Daily News. April 14, 2007.

[18]

. Mowshowitz and A. Kawaguchi. Bias on the Web.Communications of the ACM (CACM), September 2002.

Digital Library

[19]

A. Nakov and M. Hearst. Solving relational similarityproblems using the Web as a corpus. Proceedings of ACL/HLT2008.

[20]

PowerReviews. Social Shopping Study 2007. http://www.powerreviews.com

[21]

D. Quercia, S. Hailes and L. Capra. Lightweight distributed trust propagation. ICDM2007.

Digital Library

[22]

P. Resnick, R. Zeckhauser, E. Friedman, K. Kuwabara. Reputation Systems. Communications of the ACM, 2000.

Digital Library

[23]

T. Riggs and R. Wilensky. An algorithm for automatedrating of reviewers. JCDL 2001.

Digital Library

[24]

G. Sandoval. Digg continues to battle phony stories. CNET.com, December 18, 2006.

[25]

P. Turney. Expressing implicit semantic relationswithout supervision. Proceedings of ACL 2006.

Digital Library

[26]

R. Verkaik. Google sued over defamatory posts found on Web search. The Independent. June, 2007.

[27]

Yahoo! Search BOSS. http://developer.yahoo.com/search/boss/

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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Information & Contributors

Information

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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