Spammer Detection and Fake User Identification On Social Network
S Ruksana, B.Rushi kumar, KM Nikhil, R Sujatha, K Sai kumar, A Vinod kumar
Social networking sites engage millions of users around the world. The users' interactions with these
social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable
repercussions for daily life. The prominent social networking sites have turned into a target platform
for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for
example, has become one of the most extravagantly used platforms of all times and therefore allows
an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or
websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the
possibility of expanding invalid information to users through fake identities has increased that results in
the unrolling of harmful content. 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). In this
paper, we perform a review of 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 ability
to detect: (I ) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) Fake users.
The presented techniques are also compared based on various features, such as user features, content
features, graph features, structure features, and time features
