The Structure of the EU Mediasphere
Publication Date
December 08, 2010
Journal
PLOS ONE
Authors
Ilias Flaounas, Marco Turchi, Omar Ali, Nick Fyson, et al
Volume
5
Issue
12
Pages
e14243
DOI
https://dx.plos.org/10.1371/journal.pone.0014243
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0014243
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21170383
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2999531
Europe PMC
http://europepmc.org/abstract/MED/21170383
Web of Science
000285084100001
Scopus
78650096722
Mendeley
http://www.mendeley.com/research/structure-eu-mediasphere
Events
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/813175"], "description"=>"<p>This is a 2D representation of the relative positions of EU countries based on their media content. At the centre is the ‘average’ content of the EU media. The Eurozone members are coloured in blue and the rest are coloured in gray. Eurozone countries are closer to each other, and to the average EU behaviour, than non-Eurozone countries.</p>", "links"=>[], "tags"=>["computer science", "computer science/applications", "computer science/systems and control theory"], "article_id"=>483533, "categories"=>["Information And Computing Sciences", "Cancer"], "users"=>["Ilias Flaounas", "Marco Turchi", "Omar Ali", "Nick Fyson", "Tijl De Bie", "Nick Mosdell", "Justin Lewis", "Nello Cristianini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014243.g003", "stats"=>{"downloads"=>3, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_8216_co_coverage_8217_map_of_the_EU_/483533", "title"=>"The ‘co-coverage’ map of the EU.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-08 00:58:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/813243"], "description"=>"<p>Ranking of countries based on the deviation of their media from average content.</p>", "links"=>[], "tags"=>["countries", "deviation"], "article_id"=>483606, "categories"=>["Information And Computing Sciences", "Cancer"], "users"=>["Ilias Flaounas", "Marco Turchi", "Omar Ali", "Nick Fyson", "Tijl De Bie", "Nick Mosdell", "Justin Lewis", "Nello Cristianini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014243.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_countries_based_on_the_deviation_of_their_media_from_average_content_/483606", "title"=>"Ranking of countries based on the deviation of their media from average content.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-08 01:00:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/812976"], "description"=>"<p>We created the network of the top news outlets per EU country. We connected two outlets if they reported the same stories more than expected by chance as measured by chi-square testing. A high threshold on the chi-square results that maximizes the modularity of the network was used for the current plot. This network is comprised of 147 nodes (outlets) and 263 edges organized in 31 communities. Each outlet is coloured by the country of its origin. Disconnected nodes are omitted. This way the relation of the strongest connected components and countries is revealed.</p>", "links"=>[], "tags"=>["communities", "outlets", "eu"], "article_id"=>483332, "categories"=>["Information And Computing Sciences", "Cancer"], "users"=>["Ilias Flaounas", "Marco Turchi", "Omar Ali", "Nick Fyson", "Tijl De Bie", "Nick Mosdell", "Justin Lewis", "Nello Cristianini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014243.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_communities_of_news_outlets_in_the_EU_mediasphere_/483332", "title"=>"The communities of news outlets in the EU mediasphere.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-08 00:55:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/405791"], "description"=>"<div><h3>Background</h3><p>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.</p><h3>Methodology/Principal Findings</h3><p>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.</p><h3>Conclusions/Significance</h3><p>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.</p></div>", "links"=>[], "tags"=>["eu", "mediasphere"], "article_id"=>140185, "categories"=>["Information And Computing Sciences", "Cancer"], "users"=>["Ilias Flaounas", "Marco Turchi", "Omar Ali", "Nick Fyson", "Tijl De Bie", "Nick Mosdell", "Justin Lewis", "Nello Cristianini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014243", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Structure_of_the_EU_Mediasphere/140185", "title"=>"The Structure of the EU Mediasphere", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-08 00:03:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/813035"], "description"=>"<p>This is the network of the most significant relations among EU countries that cover the same stories in their media. The network has 27 nodes that correspond to the EU countries and 112 links between them. The sparseness was chosen as high as possible with the restriction that all countries must link to at least one other country.</p>", "links"=>[], "tags"=>["eu"], "article_id"=>483401, "categories"=>["Information And Computing Sciences", "Cancer"], "users"=>["Ilias Flaounas", "Marco Turchi", "Omar Ali", "Nick Fyson", "Tijl De Bie", "Nick Mosdell", "Justin Lewis", "Nello Cristianini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014243.g002", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_8216_co_coverage_8217_network_of_EU_countries_/483401", "title"=>"The ‘co-coverage’ network of EU countries.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-08 00:56:41"}

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Relative Metric

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