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09 December 2010
More than one million news articles in 22 languages have been analysed using the latest technology to pinpoint the factors that influence and shape the news agenda in 27 European countries.
The research is the result of a collaboration between Professor Justin Lewis, Head of the University’s School of Journalism, Media and Cultural Studies and Nello Cristianini, Professor of Artificial Intelligence at the University of Bristol, with an international team of researchers conducting the first large-scale content analysis of multi-lingual texts using artificial intelligence techniques - automated smart computing.
Every day hundreds of news outlets across Europe choose which story to cover from a wide and diverse selection. While each outlet may make news choices based on individual criteria, clear patterns emerge when these choices are studied across many outlets and over a long period of time.
By using automated methods from artificial intelligence and because of recent advances in machine translation and text analysis the team was able to analyse 1,370,874 articles – a sample size well beyond existing research techniques.
They discovered that chosen news content reflects national biases, as well as cultural, economic and geographic links between countries. For example outlets from countries that trade a lot with each other and are in the Eurozone are more likely to cover the same stories, as are countries that vote for each other in the Eurovision song contest. Deviation from ‘normal content’ is more pronounced in outlets of countries that do not share the Euro, or have joined the European Union later.
Professor Lewis said: "This approach has the potential to revolutionise the way we understand our media and information systems. It opens up the possibility of analysing the mediasphere on a global scale, using huge samples that traditional analytical techniques simply couldn’t countenance. It also allows us to use automated means to identify clusters and patterns of content, allowing us to reach a new level of objectivity in our analysis."
Professor Cristianini, University of Bristol added: "Automating the analysis of news content could have significant applications, due to the central role played by the news media in providing the information that people use to make sense of the world."
The researchers selected the top-ten news outlets - established by the volume of web traffic to either its leading news feed or main page - for each of the 27 EU countries. They gathered their sample from the top stories of these outlets for six months, from 1 August 2009 until 31 January 2010. The non-English language news items, which totalled 1.2 million, were translated automatically to English.
Several expected connections between countries were found such as Greece-Cyprus; Czech Republic-Slovakia; Latvia-Estonia; United Kingdom-Ireland; Belgium-France. Links between countries not explained by borders, trade or cultural relations, could be due to other factors and may form the basis of further research.
"While this approach lacks the analysis provided by people, this new research is a significant breakthrough in the study of media content due to the recent availability of millions of news articles in digital format," added Professor Lewis.
The paper, The Structure of the EU Mediasphere, is published in the issue of Public Library of Science ONE and was carried out in conjunction with the Joint Research Centre and the European Commission.
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