Social media fingerprints of unemployment

TL;DR

This study demonstrates that social media activity patterns, including mobility, diurnal rhythms, and language use, can serve as cost-effective indicators for regional unemployment rates, revealing socio-economic disparities.

Contribution

It introduces a novel approach to estimate unemployment using social media data, linking behavioral digital fingerprints to economic indicators at a regional level.

Findings

Regions with diverse mobility fluxes have lower unemployment.

Earlier diurnal rhythms correlate with lower unemployment.

More grammatically correct language use associates with lower unemployment.

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.