Benford's Law Applies To Online Social Networks

TL;DR

This paper demonstrates that Benford's Law accurately describes the distribution of first digits in user metrics across multiple online social networks, offering a tool for data validation and fraud detection.

Contribution

It is the first comprehensive study showing Benford's Law applies to various social and behavioral features in online social networks.

Findings

Friend and follower counts follow Benford's Law.

User post counts conform to Benford's distribution.

Egocentric network friend counts also follow the law.

Abstract

Benford's Law states that the frequency of first digits of numbers in naturally occurring systems is not evenly distributed. Numbers beginning with a 1 occur roughly 30\% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. We consider social data from five major social networks: Facebook, Twitter, Google Plus, Pinterest, and Live Journal. We show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same holds for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follow the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.