Estimating sequential bias in online reviews: A Kalman filtering approach

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.