Abstract
Recent wide-spread adoption of electronic and pervasive technologies has
enabled the study of human behavior at an unprecedented level, uncovering
universal patterns underlying human activity, mobility, and inter-personal
communication. In the present work, we investigate whether deviations from
these universal patterns may reveal information about the socio-economical
status of geographical regions. We quantify the extent to which deviations in
diurnal rhythm, mobility patterns, and communication styles across regions
relate to their unemployment incidence. For this we examine a country-scale
publicly articulated social media dataset, where we quantify individual
behavioral features from over 145 million geo-located messages distributed
among more than 340 different Spanish economic regions, inferred by computing
communities of cohesive mobility fluxes. We find that regions exhibiting more
diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical
styles display lower unemployment rates. As a result, we provide a simple model
able to produce accurate, easily interpretable reconstruction of regional
unemployment incidence from their social-media digital fingerprints alone. Our
results show that cost-effective economical indicators can be built based on
publicly-available social media datasets.
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