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dc.contributor.authorAshraf Khalil
dc.contributor.authorHajjdiab Hassan
dc.contributor.authorAl-Qirim Nabeel
dc.descriptionKhalil, A., Hajjdiab, H., & Al-Qirim, N. (2017). Detecting Fake Followers in Twitter: A Machine Learning Approach.en_US
dc.description.abstractTwitter popularity has fostered the emergence of a new spam marketplace. The services that this market provides include: the sale of fraudulent accounts, affiliate programs that facilitate distributing Twitter spam, as well as a cadre of spammers who execute large scale spam campaigns. In addition, twitter users have started to buy fake followers of their accounts. In this paper we present machine learning algorithms we have developed to detect fake followers in Twitter. Based on an account created for the purpose of our study, we manually verified 13000 purchased fake followers and 5386 genuine followers. Then, we identified a number of characteristics that distinguish fake and genuine followers. We used these characteristics as attributes to machine learning algorithms to classify users as fake or genuine. We have achieved high detection accuracy using some machine learning algorithms and low accuracy using others.en_US
dc.subjectMachine Learningen_US
dc.subjectFake Followeren_US
dc.titleDetecting Fake Followers in Twitter: A Machine Learning Approachen_US

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