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Online Social Networks (OSN) are increasingly being used as platform for aneffective communication, to engage with other users, and to create a socialworth via number of likes, followers and shares. Such metrics and crowd-sourcedratings give the OSN user a sense of social reputation which she tries tomaintain and boost to be more influential. Users artificially bolster theirsocial reputation via black-market web services. In this work, we identifyusers which manipulate their projected follower count using an unsupervisedlocal neighborhood detection method. We identify a neighborhood of the userbased on a robust set of features which reflect user similarity in terms of theexpected follower count. We show that follower count estimation using ourmethod has 84.2% accuracy with a low error rate. In addition, we estimate thefollower count of the user under suspicion by finding its neighborhood drawnfrom a large random sample of Twitter. We show that our method is highlytolerant to synthetic manipulation of followers. Using the deviation ofpredicted follower count from the displayed count, we are also able to detectcustomers with a high precision of 98.62%

The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count