A responsiveness model for privacy conservation against antagonist and fake user in cyber- physical and online social networks

Abstract

The enormous numbers of users are interacted with Social networking sites throughout the world. They are consistently engaging with Social Medias are Twitter, Instagram and Facebook have an incredible impression and rarely objectionable consequences for daily life. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). This paper proposes to perform a review of techniques used for detecting spammers on social media like Twitter, Facebook, Instagram, etc. Furthermore, a taxonomy of alerting spam message methodologies is presented that classifies the techniques based on their ability to detect: fake content, spam message or junk mail depends on either url or trendy news, and identifying unauthenticated users. The proposed techniques are also compared based on historical features that comprise the characteristics of user profile, wish-list, content usage, methodology used, and physical context, logging activities. This research paper contributes the study on identifying appropriate users against fake users and spam or junk mail detection using Adversary-Privacy evaluation model that helpful to preserving private data in Cyber-Physical Social Systems.