Detecting Spammers in Twitter Network

Aso Khaleel Ameen; Buket Kaya

doi:10.18100/ijamec.2017436078

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 71-75

Detecting Spammers in Twitter Network

Aso Khaleel Ameen •

Buket Kaya

Publication DateDecember 31, 2017

Volume / IssueVol. 5, No. 4 (pp. 71-75)

DOI Identifier10.18100/ijamec.2017436078

subject

Abstract

The goal of Twitter is to allow friends communicate and stay connected through the exchange of short messages. However, sometimes, spammers also use Twitter as a platform to post malicious links, send unsolicited messages to legitimate users, and hijack trending topics because of two problems of Twitter. These problems are the possibilities to automatically receive following users’ updates and to write on followers’ profile pages. For this reason, spam is becoming an increasing problem on Twitter day after day as other online social network sites are. In this article, we present several methods to detect spam tweets on Twitter. For this purpose, we utilize Naive Bayes, Random Forest J48, and IBK algorithms. The experiments conducted on real Twitter accounts demonstrate that the Random Forest algorithm gives us the best result to detect spammers in Twitter.

Keywords:Spammer DetectionSpam TweetsClassification Algorithms

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Detecting Spammers in Twitter Network

Abstract

References (20)

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