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Scaling laws in geo-located Twitter data.

Rudy Arthur1, Hywel T P Williams1

  • 1Social & Environmental Data Analysis Lab, Department of Computer Science, University of Exeter, Exeter, United Kingdom.

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Summary
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Population density accurately predicts Twitter activity, with usage scaling super-linearly with population. This finding is crucial for geospatial analyses using Twitter data and understanding demographic relationships.

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Area of Science:

  • Geographic Information Science
  • Computational Social Science
  • Social Media Analytics

Background:

  • Twitter is a vital source for high-volume spatial data on social processes.
  • Understanding the link between population density and Twitter activity is essential for accurate geospatial analysis.

Purpose of the Study:

  • To systematically investigate the relationship between population density and Twitter activity.
  • To determine if Twitter use scales linearly or non-linearly with population density.

Main Methods:

  • Analyzed the relationship between population density and Twitter metrics (number of tweets, users).
  • Investigated power law functions to model the observed relationships.
  • Examined the consistency of these relationships across different spatial scales.

Main Results:

  • A systematic, super-linear relationship was found between population density and Twitter activity.
  • Power law functions with exponents greater than one accurately describe this relationship.
  • Identified geographic areas with anomalous Twitter usage deviating from the expected trend.

Conclusions:

  • Population density is a strong predictor of Twitter activity, but requires non-linear scaling for accurate prediction.
  • The observed super-linearity impacts geospatial analyses and understanding of Twitter use demographics.
  • The relationship holds across scales and helps identify areas with unexpected Twitter engagement, independent of age structure.