Product Review Credibility Analysis

Abstract

Product reviews are vital sources of customers’ opinions and have a significant impact on purchasing decisions and product rankings on online shopping sites. Unfortunately, fraudsters (spammers) may write deceptive reviews (spam reviews) appreciating or deprecating a product, which can mislead potential customers and negatively affect revenues of many genuine organizations. Therefore, there is a great need for an effective approach to detect fake reviews and spammers. In this paper, we propose a statistical credibility scoring mechanism to identify spam reviews. It consists of three components: detection of duplicate reviews, detection of anomaly in review count and rating distribution, and detection of incentivized reviews. These three methodologies complement each other to effectively indicate the credibility of product reviews without requiring significant computational resources. It can aid data mining and online spam filtering systems to filter out spam product reviews and refine product rankings.