PROJECT TITLE :

NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media - 2017

ABSTRACT:

Today, a massive half of individuals depend on out there content in social media in their selections (e.g., reviews and feedback on a subject or product). The risk that anybody can leave a review provides a golden chance for spammers to jot down spam reviews regarding merchandise and services for various interests. Identifying these spammers and also the spam content could be a hot topic of research, and although a substantial variety of studies are done recently toward this end, but therefore far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature sort. During this paper, we tend to propose a unique framework, named NetSpam, which utilizes spam options for modeling review data sets as heterogeneous info networks to map spam detection procedure into a classification drawback in such networks. Using the importance of spam options helps us to get better results in terms of different metrics experimented on real-world review information sets from Yelp and Amazon Web sites. The results show that NetSpam outperforms the prevailing methods and among four categories of options, together with review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the primary kind of features performs better than the other categories.


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