• Ravi Verma
• Ravi Srivastava
• Shikha Tiwari
• Priyankesh Sinha

Abstract-Nowadays,a big part of people rely on available con-tent 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.

Machine Learning, Artificial Intelligence,Spam Detection.

"NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.940-947, June 2019, Available :http://www.jetir.org/papers/JETIR1907277.pdf

"NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp940-947, June 2019, Available at : http://www.jetir.org/papers/JETIR1907277.pdf