Calibration of Voting-Based Helpfulness Measurement for Online Reviews: An Iterative Bayesian Probability Approach

Xunhua Guo (), Guoqing Chen (), Cong Wang (), Qiang Wei () and Zunqiang Zhang ()
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Xunhua Guo: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China
Guoqing Chen: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China
Cong Wang: Guanghua School of Management, Peking University, Beijing 100871, China, School of Economics and Management, Tsinghua University, Beijing 100084, China
Qiang Wei: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China
Zunqiang Zhang: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 246-261

Abstract: Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

Keywords: online reviews; helpfulness prediction; social voting; Bayesian probability; iterative estimation; predictive analytics (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:246-261

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