Social media fingerprints of unemployment.

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...

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Main Authors: Alejandro Llorente, Manuel Garcia-Herranz, Manuel Cebrian, Esteban Moro
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4447438?pdf=render
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author Alejandro Llorente
Manuel Garcia-Herranz
Manuel Cebrian
Esteban Moro
author_facet Alejandro Llorente
Manuel Garcia-Herranz
Manuel Cebrian
Esteban Moro
author_sort Alejandro Llorente
collection DOAJ
description 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.
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spelling doaj.art-24519a27cc2b494088895610147c763b2022-12-21T19:26:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01105e012869210.1371/journal.pone.0128692Social media fingerprints of unemployment.Alejandro LlorenteManuel Garcia-HerranzManuel CebrianEsteban MoroRecent 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.http://europepmc.org/articles/PMC4447438?pdf=render
spellingShingle Alejandro Llorente
Manuel Garcia-Herranz
Manuel Cebrian
Esteban Moro
Social media fingerprints of unemployment.
PLoS ONE
title Social media fingerprints of unemployment.
title_full Social media fingerprints of unemployment.
title_fullStr Social media fingerprints of unemployment.
title_full_unstemmed Social media fingerprints of unemployment.
title_short Social media fingerprints of unemployment.
title_sort social media fingerprints of unemployment
url http://europepmc.org/articles/PMC4447438?pdf=render
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AT manuelgarciaherranz socialmediafingerprintsofunemployment
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