Social media fingerprints of 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.

19 pages (8 main article, 11 Supplementary Information)

References in corpus (11)

• Fast unfolding of communities in large networks
• Understanding individual human mobility patterns
• Finding community structure in networks using the eigenvectors of matrices
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Comparing community structure identification
• Geo-located Twitter as the proxy for global mobility patterns
• Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data
• Cross-checking different sources of mobility information
• Quantifying long-term evolution of intra-urban spatial interactions
• The Readability of Tweets and their Geographic Correlation with Education

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