Towards online review spam detection

Yuming Lin, Tao Zhu, Xiaoling Wang, Jingwei Zhang, Aoying Zhou

Abstract: User reviews play a crucial role in Web, since many decisions are made based on them. However, review spam would misled the users, which is extremely obnoxious. In this poster, we explore the problem of online review spam detection. Firstly, we devise six features to find the spam based on the review content and reviewer behaviors. Secondly, we apply supervised methods and an unsupervised one for spotting the review spam as early as possible. Finally, we carry out intensive experiments on a real-world review set to verify the proposed methods.