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Discovering the Fake Followers in the Micro-blogging via Machine Learning

Discovering the Fake Followers in the Micro-blogging via Machine Learning. Yi Shen Jianjun Yu October 16, 2013. Computer Network Information Center, Chinese Academy of Sciences. Micro-blogging. 2. The Celebrities in Twitter. 3. Purchasing Fake Followers. Fake followers Markets.

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Discovering the Fake Followers in the Micro-blogging via Machine Learning

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  1. Discovering the Fake Followers in the Micro-blogging via Machine Learning Yi Shen Jianjun Yu October 16, 2013 Computer Network Information Center, Chinese Academy of Sciences

  2. Micro-blogging 2

  3. The Celebrities in Twitter 3

  4. Purchasing Fake Followers

  5. Fake followers Markets

  6. A Data Report 39% of @facebook followers are fake 34% of @ladygaga followers are fake  31% of @justinbieber followers are fake  32% of @katyperry followers are fake  32% of @espn followers are fake 33% of @britneyspears followers are fake  27% of @youtube followers are fake  http://www.socialsellingu.com/fake-twitter-profiles-infographic/

  7. Troubles Caused by Fake Followers • Noise for Social Network analysis • Privacy and Security Problem • Spam Problem

  8. Method of Detection • Binary Classification Problem • Extract discriminative features • Voting-SVM as the classifier

  9. How to get ground-truth data? • Purchase from different merchants • Keep tracking them for a long period

  10. The Features for Classification • The Ratio of Followee Count and Follower Count (RFF) • The Percentage of Bidirectional Friends (PBF) • Average Repost Frequency of the Posts (ARF) • Ratio of the Original Posts (ROP) • Proportion of Nighttime Posts (PNP) • Topic Diversity • The standard deviation of post-count(σpost). • The general slope of post-count(gpost). • The standard deviation of followee-count(σ followee) • The decrease frequency of followee-count(DFfollowee). • The standard deviation of follower-count (σfollower).

  11. Result

  12. Thank you!

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