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.
Highlights
Reliable and effective monitoring of the worldwide mobility patterns plays an important role in studies exploring migration flows (Castles and Mill 1998; Greenwood 1985; Sassen 1999), touristic activity (Miguéns and Mendes 2008) and the spread of diseases and epidemic modeling (Bajardi et al 2011; Balcan et al 2009)
Geo-located Twitter is one of the first free and available global data sources that store millions of digital and fully objective records of human activity located in space and time
With our study we demonstrated that, despite the unequal distribution over the different parts of the world and possible bias toward a certain part of the population, in many cases geo-located Twitter can and should be considered as a valuable proxy for human mobility, especially at the level of country-to-country flows
Summary
Reliable and effective monitoring of the worldwide mobility patterns plays an important role in studies exploring migration flows (Castles and Mill 1998; Greenwood 1985; Sassen 1999), touristic activity (Miguéns and Mendes 2008) and the spread of diseases and epidemic modeling (Bajardi et al 2011; Balcan et al 2009). Definition of a country of the user’s residence An essential first step in our cross-country mobility analysis was an explicit assignment of each user to a country of residence This made our work different from most of the other Twitter studies, which usually did not attempt to uncover users’ origin and characterized a study area using only the total volume of tweets observed in this area The fact that clusters were spatially continuous and reflected common regions of the world is in line with the findings of previous studies based on the mobile phone data (Ratti et al 2010; Blondel et al 2010; Blondel 2011; Sobolevsky et al 2013a) It extends the validity of network-based community detection form a country to global scale. We see that the partitioning of mobility networks possess similar regularities compared to human communication networks and might be used for regional delineation purposes
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