Twitter as a Source of Global Mobility Patterns for Social Good

2016/06/20 by Mark Dredze, Dredze, Mark, Manuel García–Herranz +5
Medicine · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Machine Learning (stat.ML) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.1606.06343

openalex publication_date 2016/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geolocation data from Twitter can be used to estimate global mobility patterns and address these shortcomings. These findings will inform how this novel data source can be harnessed to address humanitarian and development efforts.

Citations

• Geotagging one hundred million Twitter accounts with total variation minimization

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