Survey on Spam Filtering Using Netspam Framework

Abstract

Majority of people uses internet and trust’s the contents over it. The scenario where anyone can bring out a survey gives an open edge to the spammer to generate fake surveys about products and services. Identification of these intruder and fake contain is a widely debated issue of research as of now tremendous amount of studies has been done till now, then also the existing work lacks behind in differentiating spam reviews and none of them gives the significant result to the collected feature type. This application uses a new structure, called NetSpam, which offers spam features to demonstrate product review data sets as heterogeneous information networks in order to design a spam review detection method in such networks. Using the importance of spam features helps us to achieve better results on review data sets with respect to different metrics. The outcomes represent that NetSpam results with the previous methods and encompassed by four categories of features: The first type of features performs better than the other categories, involving review - behavioral, user - behavioral, linguistic review and user - linguistic.