  Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories.
