This paper is published in Volume-4, Issue-3, 2018
Area
Cyber Forensic
Author
Shraddha Autade
Co-authors
Mrunali Donde, Kiran Pawar, Himanshu Singhal, Prashant Ahire
Org/Univ
Dr. D. Y. Patil Institute of Technology, Pune, Maharashtra, India
Pub. Date
08 May, 2018
Paper ID
V4I3-1239
Publisher
Keywords
Social media, Social network, Spammer, Spam review, Fake review, Heterogeneous information networks.

Citationsacebook

IEEE
Shraddha Autade, Mrunali Donde, Kiran Pawar, Himanshu Singhal, Prashant Ahire. Detection and controlling measures on net-spam on social media, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shraddha Autade, Mrunali Donde, Kiran Pawar, Himanshu Singhal, Prashant Ahire (2018). Detection and controlling measures on net-spam on social media. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Shraddha Autade, Mrunali Donde, Kiran Pawar, Himanshu Singhal, Prashant Ahire. "Detection and controlling measures on net-spam on social media." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

In recent years, online reviews have become the most important resource of customers' opinions. These reviews are used increasingly by individuals and organizations to make a purchase and business decisions the fundamental truth of large-scale data sets is not yet available and most existing approaches to supervised learning are based on pseudo- false. Identifying these spammers and spam content is a very hot topic of research, and although a large number of studies have been conducted recently for this purpose, the methodologies presented so far have barely detected spam reviews, and no one of them shows the importance of any type of extracted feature. In this study, we propose a new framework, called NetSpam, which uses spam features to model audit datasets as heterogeneous information networks to map the spam detection procedure into a classification problem in those networks. Using the importance of spam features helps us get better results in terms of different metrics experienced in real-time data sets from Yelp and Amazon sites.