Scaling laws in geo-located Twitter data

TL;DR

This paper uncovers power-law relationships between population density and Twitter activity, enabling predictions of Twitter use and identification of anomalies across different spatial scales.

Contribution

It reveals consistent power-law scaling laws linking population density and Twitter metrics, and distinguishes characteristics of geo-tagged versus place-tagged tweets.

Findings

Power-law relationships with exponents > 1 between population density and Twitter activity.

Population density can predict Twitter activity accurately across scales.

Geo-tagged and place-tagged tweets differ in user type and content.

Abstract

We observe and report on a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power laws, with exponents greater than one, that are consistent with each other and across a range of spatial scales. This implies that population density can accurately predict Twitter activity. Furthermore this trend can be used to identify `anomalous' areas that deviate from the trend. Analysis of geo-tagged and place-tagged tweets show that geo-tagged tweets are different with respect to user type and content. Our findings have implications for the spatial analysis of Twitter data and for understanding demographic biases in the Twitter user base.