Detecting the Internet Water Army via comprehensive behavioral features using large-scale E-commerce reviews

Abstract

Online reviews play a crucial role in helping consumers to make purchase decisions. However, a severe problem Internet Water Army (a large amount of paid posters who write inauthentic reviews) emerge in many E-commerce websites recently which dramatically undermines the value of user reviews. Although the word Internet Water Army originated from China, some other countries also suffered from this problem. Many organized underground paid poster groups found it extremely profitable to mislead the consumers by writing fake reviews. It had become more and more challenging to accurately detect the water army who could alter their writing style. In this paper, we design a comprehensive set of features to compare paid posters against normal users on different dimensions. Then we build an ensemble detection model of seven different algorithms. Our model has reached 0.726 in AUC measure and 0.683 in F1 measure on JD dataset, 0.926 in AUC measure and 0.871 in F1 measure on Amazon dataset, which outperforms previous studies. Our work provides some practical solutions and guidance to this severe problem for the whole E-commerce industry.