article

Estimating sequential bias in online reviews: A Kalman filtering approach

Authors:

Riyaz T. Sikora,

Kriti ChauhanAuthors Info & Claims

Knowledge-Based Systems, Volume 27

Pages 314 - 321

https://doi.org/10.1016/j.knosys.2011.10.011

Published: 01 March 2012 Publication History

Abstract

Online reviews of products along with reviewer related data are regarded by many as one of the most significant knowledge base systems created by online commerce websites. They have played a big role in fueling the popularity and growth of electronic marketplaces like Amazon and eBay. Although the main attraction of online reviews is that they are perceived by most consumers to be independent and unbiased, many studies have shown the existence of various types of biases inherent in the product reviews. In this paper we present a novel approach of estimating the bias in reviews using Kalman filtering technique that is computationally feasible and can update the estimation of bias with every new review without having to store all the past ratings information. We further extend our model to study the existence of sequential bias in the reviews. We use panel data from 19 different products collected from Amazon.com and show the existence of sequential bias in ratings that depends on previous review and reviewer characteristics.

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Cited By

Zhu QLo LXia MChen ZMa X(2022)Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and ReviewsProceedings of the ACM on Human-Computer Interaction10.1145/35555976:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555597
Behera RBala PRana NKizgin H(2022)A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B PerspectiveInformation Systems Frontiers10.1007/s10796-021-10231-824:6(2027-2051)Online publication date: 15-Jan-2022
https://dl.acm.org/doi/10.1007/s10796-021-10231-8
Eryarsoy EPiramuthu S(2014)Experimental evaluation of sequential bias in online customer reviewsInformation and Management10.1016/j.im.2014.09.00151:8(964-971)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.im.2014.09.001
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Copyright © Elsevier B.V. © 2011.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2012

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Cited By

Zhu QLo LXia MChen ZMa X(2022)Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and ReviewsProceedings of the ACM on Human-Computer Interaction10.1145/35555976:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555597
Behera RBala PRana NKizgin H(2022)A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B PerspectiveInformation Systems Frontiers10.1007/s10796-021-10231-824:6(2027-2051)Online publication date: 15-Jan-2022
https://dl.acm.org/doi/10.1007/s10796-021-10231-8
Eryarsoy EPiramuthu S(2014)Experimental evaluation of sequential bias in online customer reviewsInformation and Management10.1016/j.im.2014.09.00151:8(964-971)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.im.2014.09.001
You LSikora R(2014)Performance of online reputation mechanisms under the influence of different types of biasesInformation Systems and e-Business Management10.1007/s10257-013-0229-912:3(417-442)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1007/s10257-013-0229-9
Alguliev RAliguliyev RIsazade N(2012)DESAMC+DocSumKnowledge-Based Systems10.1016/j.knosys.2012.05.01736(21-38)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1016/j.knosys.2012.05.017

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