Geo-located Twitter as proxy for global mobility patterns

TLDR

Location‑sharing services provide unprecedented direct records of human activity in space and time. The study analyzes geo‑located Twitter messages to uncover global human mobility patterns. Using nearly a billion 2012 tweets, the authors estimate international traveler volumes by country of residence and characterize national mobility profiles by rate, radius of gyration, destination diversity, and inflow–outflow balance. Temporal analysis reveals universal seasonal peaks in international mobility and distinct national travel patterns, while community detection identifies spatially cohesive regions that align with world divisions; these results, validated against tourism statistics and other models, demonstrate Twitter’s exceptional utility for quantifying global mobility.

Abstract

Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.