<![CDATA[In this paper, a new method has been proposed for identifying anonymous fake reviews generated by click farmers in E-commerce and improves the identification rates. Anonymous fake reviews are different from the gunuine reviews. They could be distinguished based on the credibility of users, the average daily number of evaluations, the content similarity, and the degree of word overlapping. The proposed method takes into account these 5 features to calculate the fake reviews content by constructing multivariate linear regression model, Experiments show that this prelimilnary work performed well in identifying fake reviews in Chinese E-commerce website. The extracted features are also useful to identifying the fake reviews when the reviewer’s identification is not accessable.]]>
