From Twitter to GDP: Estimating Economic Activity From Social Media

[EN] This paper shows how the use of data derived from Twitter can be used as a proxy for measuring GDP at the country level. Using a dataset of 270 million geo-located image tweets shared on Twitter in 2012 and 2013, I find that: (i) Twitter data can be used as a proxy for estimating GDP at the cou...

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Detalles Bibliográficos
Autor: Indaco, Agustín
Tipo de recurso: capítulo de libro
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/112097
Acceso en línea:https://riunet.upv.es/handle/10251/112097
Access Level:acceso abierto
Palabra clave:Web data
Internet data
Big data
QCA
PLS
SEM
Conference
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repository_id_str
spelling From Twitter to GDP: Estimating Economic Activity From Social MediaIndaco, AgustínWeb dataInternet dataBig dataQCAPLSSEMConference[EN] This paper shows how the use of data derived from Twitter can be used as a proxy for measuring GDP at the country level. Using a dataset of 270 million geo-located image tweets shared on Twitter in 2012 and 2013, I find that: (i) Twitter data can be used as a proxy for estimating GDP at the country level and can explain 94 percent of the variation in GDP; and (ii) that the residuals from my preferred model are negatively correlated to a data quality index which assesses the capacity of a country’s statistical system. This suggests that my estimates for GDP are more accurate for countries which are considered to have more reliable GDP data. Taken together, these findings show that institutions and individuals could use social media data to corroborate official GDP estimates; or alternatively for government statistic agencies to incorporate social media data to complement and further reduce measurement errors.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20182018-09-07book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/112097reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1120972026-06-13T07:49:27Z
dc.title.none.fl_str_mv From Twitter to GDP: Estimating Economic Activity From Social Media
title From Twitter to GDP: Estimating Economic Activity From Social Media
spellingShingle From Twitter to GDP: Estimating Economic Activity From Social Media
Indaco, Agustín
Web data
Internet data
Big data
QCA
PLS
SEM
Conference
title_short From Twitter to GDP: Estimating Economic Activity From Social Media
title_full From Twitter to GDP: Estimating Economic Activity From Social Media
title_fullStr From Twitter to GDP: Estimating Economic Activity From Social Media
title_full_unstemmed From Twitter to GDP: Estimating Economic Activity From Social Media
title_sort From Twitter to GDP: Estimating Economic Activity From Social Media
dc.creator.none.fl_str_mv Indaco, Agustín
author Indaco, Agustín
author_facet Indaco, Agustín
author_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Web data
Internet data
Big data
QCA
PLS
SEM
Conference
topic Web data
Internet data
Big data
QCA
PLS
SEM
Conference
description [EN] This paper shows how the use of data derived from Twitter can be used as a proxy for measuring GDP at the country level. Using a dataset of 270 million geo-located image tweets shared on Twitter in 2012 and 2013, I find that: (i) Twitter data can be used as a proxy for estimating GDP at the country level and can explain 94 percent of the variation in GDP; and (ii) that the residuals from my preferred model are negatively correlated to a data quality index which assesses the capacity of a country’s statistical system. This suggests that my estimates for GDP are more accurate for countries which are considered to have more reliable GDP data. Taken together, these findings show that institutions and individuals could use social media data to corroborate official GDP estimates; or alternatively for government statistic agencies to incorporate social media data to complement and further reduce measurement errors.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-09-07
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/112097
url https://riunet.upv.es/handle/10251/112097
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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