Spammer Detection and Fake User Identification on Social Networks

Abstract

Millions of people utilise social networking services all huge influence on daily life, with some unfavorable consequences. Spammers have converted popular social networking sites into a target platform for disseminating a large number of useless and harmful material. allowing for an excessive quantity of spam. Fake users send unwanted tweets to users in order to advertise services or websites, which not only harm actual users but also waste resources. Furthermore, the ability of spreading false information to users via fake identities has grown, resulting in the spread of hazardous materials. Twitter has recently been a popular study topic in today's online social networks (OSNs). We examine the approaches used to detect spammers on Twitter in this research. Furthermore, a taxonomy of Twitter spam detection systems is offered, which divides the strategies into four categories such as user characteristics, content characteristics, graph characteristics, structural characteristics, and temporal characteristics. We believe that the research given here will serve as a valuable resource for scholars looking for the latest breakthroughs in Twitter spam detection in one place.