Social media fingerprints of unemployment

PLoS One. 2015 May 28;10(5):e0128692. doi: 10.1371/journal.pone.0128692. eCollection 2015.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Datasets as Topic
  • Female
  • Humans
  • Male
  • Models, Theoretical*
  • Social Media*
  • Socioeconomic Factors
  • Spain
  • Unemployment*

Grant support

Partial funding came from the Spanish Ministry of Economy and Competitiveness through grant FIS2013-47532-C3-3-P, the Australian Government, and the Australian Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.