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...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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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. |
first_indexed | 2024-12-20T21:32:01Z |
format | Article |
id | doaj.art-24519a27cc2b494088895610147c763b |
institution | Directory Open Access Journal |
issn | 1932-6203 |
language | English |
last_indexed | 2024-12-20T21:32:01Z |
publishDate | 2015-01-01 |
publisher | Public Library of Science (PLoS) |
record_format | Article |
series | PLoS ONE |
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 |
work_keys_str_mv | AT alejandrollorente socialmediafingerprintsofunemployment AT manuelgarciaherranz socialmediafingerprintsofunemployment AT manuelcebrian socialmediafingerprintsofunemployment AT estebanmoro socialmediafingerprintsofunemployment |