Scaling laws in geo-located Twitter data
This helps researchers analyze spatial Twitter data and understand demographic biases in its user base.
The study found that population density predicts Twitter activity through consistent power-law relationships across spatial scales, with exponents greater than one, and identified anomalous areas deviating from this trend.
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.