Trending Topics based Spam Detection from the Social Media

Abstract

With an increasing usage of social media to exchange, share and store information, cybercriminals also get attracted to it, to take advantage of the network for their illegal and unethical benefits. Fake online accounts pop up every day. Spammers are the users behind the screen who share unsolicited and irrelevant texts to a huge number of users with an intent of advertising some product or to make people to click on unsecured links and infecting user’s system usually to make money (click bait). They often use Trending topics on social media as a medium to spam. Sometimes, spam and fake trending is created by Spammers and many a times spammers use Trending topics to lure victims into clicking them. Much research has been done and is going on to detect spammers in OSNs. This paper reviews the existing techniques to detect spammers in social media. Our Current study and future work provides an overview of the traditional classifiers, Naive Bayes, Support Vector and how they are used to detect spam and classify a dataset taken from social media into trending and non-trending topics based spam.