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

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 neighborhood drawn from a large random sample of Twitter. We show that our method is highly tolerant to synthetic manipulation of followers. Using the deviation of predicted follower count from the displayed count, we are also able to detect customers with a high precision of 98.62%