The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press

To locate media frames is one of the biggest challenges facing academics in Political Communication disciplines. The traditional approach to the problem is the use of different coders and their subsequent comparison, either through statistical analysis, or through agreements between them. In both ca...

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Detalles Bibliográficos
Autores: García-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3, Calatrava, A. (Adolfo)|||/items/12e90791-f38e-41ee-8b11-063d616a0fb7
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/55789
Acceso en línea:https://hdl.handle.net/10171/55789
Access Level:acceso abierto
Palabra clave:Algorithms
Framing
Press
Spain
SVM
Refugees
Refugee crisis
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spelling The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the PressGarcía-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3Calatrava, A. (Adolfo)|||/items/12e90791-f38e-41ee-8b11-063d616a0fb7AlgorithmsFramingPressSpainSVMRefugeesRefugee crisisTo locate media frames is one of the biggest challenges facing academics in Political Communication disciplines. The traditional approach to the problem is the use of different coders and their subsequent comparison, either through statistical analysis, or through agreements between them. In both cases, problems arise due to the difficulty of defining exactly where the frame is as well as its meaning and implications. And, above all, it is a complex process that makes it very difficult to work with large data sets. The authors, however, propose the use of information cataloging algorithms as a way to solve these problems. These algorithms (Support Vector Machines, Random Forest, CNN, etc.) come from disciplines linked to neural networks and have become an industry standard devoted to the treatment of non-numerical information and natural language processing. In addition, when supervised, they can be trained to find the information that the researcher considers pertinent. The authors present one case study, the media framing of the refugee crisis in Europe (in 2015) as an example. In that regard, SVM shows a lot of potential, being able to locate frames successfully albeit with some limitations.Servicio de Publicaciones de la Universidad de NavarraDadun. Depósito Académico Digital Universidad de Navarra20182018-11-1320182018-01-0120182018-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/55789reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/557892026-06-21T12:47:57Z
dc.title.none.fl_str_mv The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
title The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
spellingShingle The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
García-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3
Algorithms
Framing
Press
Spain
SVM
Refugees
Refugee crisis
title_short The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
title_full The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
title_fullStr The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
title_full_unstemmed The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
title_sort The Use of Supervised Learning Algorithms in Political Communication and Media Studies: Locating Frames in the Press
dc.creator.none.fl_str_mv García-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3
Calatrava, A. (Adolfo)|||/items/12e90791-f38e-41ee-8b11-063d616a0fb7
author García-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3
author_facet García-Marín, J. (Javier)|||/items/cb01f0ad-183d-4afc-bc6b-bae6c505d6e3
Calatrava, A. (Adolfo)|||/items/12e90791-f38e-41ee-8b11-063d616a0fb7
author_role author
author2 Calatrava, A. (Adolfo)|||/items/12e90791-f38e-41ee-8b11-063d616a0fb7
author2_role author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Algorithms
Framing
Press
Spain
SVM
Refugees
Refugee crisis
topic Algorithms
Framing
Press
Spain
SVM
Refugees
Refugee crisis
description To locate media frames is one of the biggest challenges facing academics in Political Communication disciplines. The traditional approach to the problem is the use of different coders and their subsequent comparison, either through statistical analysis, or through agreements between them. In both cases, problems arise due to the difficulty of defining exactly where the frame is as well as its meaning and implications. And, above all, it is a complex process that makes it very difficult to work with large data sets. The authors, however, propose the use of information cataloging algorithms as a way to solve these problems. These algorithms (Support Vector Machines, Random Forest, CNN, etc.) come from disciplines linked to neural networks and have become an industry standard devoted to the treatment of non-numerical information and natural language processing. In addition, when supervised, they can be trained to find the information that the researcher considers pertinent. The authors present one case study, the media framing of the refugee crisis in Europe (in 2015) as an example. In that regard, SVM shows a lot of potential, being able to locate frames successfully albeit with some limitations.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-11-13
2018
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/55789
url https://hdl.handle.net/10171/55789
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
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Servicio de Publicaciones de la Universidad de Navarra
publisher.none.fl_str_mv Servicio de Publicaciones de la Universidad de Navarra
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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