The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count
Anupama Aggarwal, Saravana Kumar, Kushagra Bhargava, Ponnurangam, Kumaraguru

TL;DR
This paper presents an unsupervised method to detect Twitter users manipulating their follower counts, achieving high accuracy and precision, thereby addressing the issue of artificially inflated social reputation metrics.
Contribution
The authors introduce a novel neighborhood-based detection technique that accurately identifies manipulated follower counts with high tolerance to synthetic manipulations.
Findings
84.2% accuracy in follower count estimation
High detection precision of 98.62%
Method is tolerant to synthetic follower manipulation
Abstract
Online Social Networks (OSN) are increasingly being used as platform for an effective communication, to engage with other users, and to create a social worth via number of likes, followers and shares. Such metrics and crowd-sourced ratings give the OSN user a sense of social reputation which she tries to maintain and boost to be more influential. Users artificially bolster their social reputation via black-market web services. In this work, we identify users which manipulate their projected follower count using an unsupervised local neighborhood detection method. We identify a neighborhood of the user based on a robust set of features which reflect user similarity in terms of the expected follower count. We show that follower count estimation using our method has 84.2% accuracy with a low error rate. In addition, we estimate the follower count of the user under suspicion by finding its…
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Taxonomy
TopicsSpam and Phishing Detection · Network Security and Intrusion Detection · Complex Network Analysis Techniques
