Identification of Spammers and Fake Users on Social Networks

Abstract

A great many users are engaged with social networking sites the world over. Social sites like Twitter,
Facebook have a large effect on rare unwanted consequences caused in our regular life in user's
interactions. To disperse a large measure of inappropriate and destructive information distending social
networking sites are made as a target stage for the spammers. Twitter is the fundamental example that
has become one of the significant stages for an unreasonable measure of spam in all the tomes for fake
users to tweet and promote websites or services that create a significant effect for legitimate users and
furthermore it upsets resources utilization. By resulting in the opening for surprising and unsafe data
there is an increase of fake identities that expands invalid information. Research on current online social
networks (OSN) is quite normal for identifying spammers and furthermore detection of fake users on
twitter. Recently, the detection of spammers and the identification of fake users on Twitter has become a
typical area of research in contemporary online social networks (OSNs). In this paper, we review the
techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection
approaches is presented that classifies the techniques based on their capacity to detect: (I) fake content,
(ii) spam based on URL, (iii) spam in trending points, and (iv) fake users. The presented techniques are
likewise compared based on different features, for example, user features, content features, chart
features, structure features, and time features. We are hopeful that the presented investigation will be a
useful resource for researchers to nd the features of recent developments in Twitter spam detection on a
single stage.