Divining Unemployment Patterns from Social Media Data
January 14, 2015
It is now possible to map regional unemployment estimates based solely on social-media data. That’s the assertion of a little write-up posted by Cornell University Library titled, “Social Media Fingerprints of Unemployment.” Researchers Alejandro Llorente, Manuel Garcia-Harranze, Manuel Cebrian, and Esteban Moro reveal:
“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.”
The team used these patterns to create a model they say paints an accurate picture of regional unemployment incidence. They assure us that these results can be created at low-cost using publicly available data from social media sources. Click here (PDF) to view the team’s paper on the subject.
Cynthia Murrell, January 14, 2015
Sponsored by ArnoldIT.com, developer of Augmentext
Comments
-
- Subscribe to Beyond Search
Feature archive
News archive
-
