Recent widespread 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 interpersonal 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 19 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.
Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid 28049, Spain;Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, Leganés 28911, Spain;UNICEF Innovation Unit, New York, NY 10017, USA;Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA;National Information and Communications Technology Australia, Melbourne, Victoria 3003, Australia;Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid 28049, Spain;Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, Leganés 28911, Spain
Recommended Citation:
Alejandro Llorente,Manuel Garcia-Herranz,Manuel Cebrian,et al. Social Media Fingerprints of Unemployment[J]. PLOS ONE,2015-01-01,10(5)