Scaling in Words on Twitter

Eszter Bokányi¹, Dániel Kondor^2,3 & Gábor Vattay<sup>1

1</sup>Department of Physics of Complex Systems, Eötvös Loránd University, 1117 Budapest, Hungary
²Senseable City Laboratory, MIT, Cambridge MA 02139 USA
³Singapore-MIT Alliance for Research and Technology, Singapore

Abstract

Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the Metropolitan and Micropolitan Statstical Areas of the United States. We observe a slightly superlinear urban scaling decreasing with the city population for the total volume of the tweets and words created in a city. When find that a certain core vocabulary follows the scaling relationship of that of the bulk text, but most words are sensitive to city size, exhibiting a super- or a sublinear urban scaling. In both regimes, the meaning of the most superlinearly or most sublinearly scaling words is representative of their exponent. We also show that the parameters for Zipf's law and Heaps law differ on Twitter from that of other texts, and that the exponent of Zipf's law changes with city size.

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Raw word count data ordered by MSAs CSV

Fitted data and power-law urban scaling results for aggregate metrics JSON

Fitted data and power-law urban scaling results for each word JSON

Data for Zipf's law Total | Citywise