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Scaling in words on Twitter.

Eszter Bokányi1, Dániel Kondor2,3, Gábor Vattay1

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This study reveals how language scales with city size on Twitter. While total tweet volume scales superlinearly, most individual words show size-dependent scaling, offering insights into urban communication patterns.

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Area of Science:

  • Computational linguistics
  • Urban informatics
  • Social network analysis

Background:

  • Language scaling properties offer insights into text generation.
  • Urban scaling laws describe how various urban metrics change with population size.
  • Twitter data provides a unique corpus for studying language in urban contexts.

Purpose of the Study:

  • To investigate urban scaling laws in Twitter corpora across US metropolitan and micropolitan areas.
  • To analyze how vocabulary usage and word frequency distributions vary with city population on Twitter.
  • To compare Twitter's linguistic scaling properties with those of other text types.

Main Methods:

  • Analysis of large-scale Twitter corpora categorized by US urban statistical areas.
  • Application of statistical methods to examine word frequency distributions (Zipf's Law) and vocabulary richness (Heaps' Law).
  • Correlation analysis between linguistic metrics and city population size.

Main Results:

  • Observed slightly superlinear urban scaling for total tweet and word volume with city population.
  • Identified a core vocabulary following bulk text scaling, while most words exhibited size-dependent (super- or sublinear) urban scaling.
  • Found Zipf's Law and Heaps' Law parameters on Twitter differ from other texts, with Zipf's Law exponent varying by city size.

Conclusions:

  • Linguistic behavior on Twitter demonstrates distinct urban scaling patterns compared to traditional texts.
  • Word usage sensitivity to city size suggests varying communication dynamics across urban populations.
  • The study provides a quantitative framework for understanding language evolution in digital urban environments.