Review spam detection via temporal pattern discovery
Published 12 August 2012
Sihong Xie, Guan Wang, Shuyang Lin, Philip S. Yu
Citations400
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TL;DR
It is discovered that singleton review is a significant source of spam reviews and largely affects the ratings of online stores and a hierarchical algorithm is proposed to robustly detect the time windows where such attacks are likely to have happened.
Abstract
Online reviews play a crucial role in today's electronic commerce. It is desirable for a customer to read reviews of products or stores before making the decision of what or from where to buy. Due to the pervasive spam reviews, customers can be misled to buy low-quality products, while decent stores can be defamed by malicious reviews. We observe that, in reality, a great portion (> 90% in the data we study) of the reviewers write only one review (singleton review). These reviews are so enormous in number that they can almost determine a store's rating and impression. However, existing methods did not examine this larger part of the reviews. Are most of these singleton reviews truthful ones? If not, how to detect spam reviews in singleton reviews? We call this problem singleton review spam detection.
Keywords
Computer Science
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