Authors:Alejandro Llorente, Manuel García-Herránz, Manuel Cebrián and Esteban Moro
Journal: PLoS ONE 10(5): e0128692 (2014) LINK
Summary: Publicly available social media data can be used to quantify deviations from typical patterns of behavior and uncover how these deviations signal the socio-economical status of regions. Using data from geolocalized Twitter messages, we find that unemployment is correlated with technology adoption, daily activity, diversity in mobility patterns and correctness in communication style. These behavioral metrics serve to build simple, interpretable, and cost-effective socio-economical predictors from these novel digital datasets. ...