The structure of the EU mediasphere.

<h4>Background</h4>A trend towards automation of scientific research has recently resulted in what has been termed "data-driven inquiry" in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the hum...

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Autores principales:	Ilias Flaounas, Marco Turchi, Omar Ali, Nick Fyson, Tijl De Bie, Nick Mosdell, Justin Lewis, Nello Cristianini
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/889f7c7933e140309aea06c39be0e443
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id	oai:doaj.org-article:889f7c7933e140309aea06c39be0e443
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spelling	oai:doaj.org-article:889f7c7933e140309aea06c39be0e4432021-11-18T07:01:56ZThe structure of the EU mediasphere.1932-620310.1371/journal.pone.0014243https://doaj.org/article/889f7c7933e140309aea06c39be0e4432010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21170383/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>A trend towards automation of scientific research has recently resulted in what has been termed "data-driven inquiry" in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not.<h4>Methodology/principal findings</h4>In this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU.<h4>Conclusions/significance</h4>While independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis.Ilias FlaounasMarco TurchiOmar AliNick FysonTijl De BieNick MosdellJustin LewisNello CristianiniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 12, p e14243 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ilias Flaounas Marco Turchi Omar Ali Nick Fyson Tijl De Bie Nick Mosdell Justin Lewis Nello Cristianini The structure of the EU mediasphere.
description	<h4>Background</h4>A trend towards automation of scientific research has recently resulted in what has been termed "data-driven inquiry" in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not.<h4>Methodology/principal findings</h4>In this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU.<h4>Conclusions/significance</h4>While independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis.
format	article
author	Ilias Flaounas Marco Turchi Omar Ali Nick Fyson Tijl De Bie Nick Mosdell Justin Lewis Nello Cristianini
author_facet	Ilias Flaounas Marco Turchi Omar Ali Nick Fyson Tijl De Bie Nick Mosdell Justin Lewis Nello Cristianini
author_sort	Ilias Flaounas
title	The structure of the EU mediasphere.
title_short	The structure of the EU mediasphere.
title_full	The structure of the EU mediasphere.
title_fullStr	The structure of the EU mediasphere.
title_full_unstemmed	The structure of the EU mediasphere.
title_sort	structure of the eu mediasphere.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/889f7c7933e140309aea06c39be0e443
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The structure of the EU mediasphere.

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