Detecting fake followers in twitter: A machine learning approach

Ashraf Khalil, Hassan Hajjdiab, Nabeel Al-Qirim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Twitter 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.

Original languageEnglish
Pages (from-to)198-202
Number of pages5
JournalInternational Journal of Machine Learning and Computing
Volume7
Issue number6
DOIs
Publication statusPublished - Dec 1 2017

Keywords

  • Fake follower
  • Machine learning
  • Security
  • Social networks
  • Twitter

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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