The Structure of the EU Mediasphere

Abstract

BackgroundA 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.Methodology/Principal FindingsIn 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.Conclusions/SignificanceWhile 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.

Research Methods in the Age of Digital Journalism

“…Similar studies have been conducted describing how machine translation can be used to access the content of all key news outlets of Europe for one year (Flaounas et al 2010), showing that content similarities among countries reflect cultural, economic and geographic ties. This kind of research demands a scale and scope that is beyond the reach of traditional content analysis.…”

“…The data collection and analysis was based on our system, described in detail in Flaounas et al (2011). This system has been used successfully before for several media analysis studies such as the analyses of factors that affect the choices of media editors (Flaounas et al 2010). Each article in our corpus was classified automatically without human interaction (using SVMs) into 15 different generic news categories such as "Crime" or "Sport".…”

Early fragmentation in the adaptive voter model on directed networks

“…With the increasing diversity of offline and online media [6,7] and new media technologies [8], it is becoming easier to avoid opposing opinions altogether. In particular, the Internet enables people not only to access but also to publish information easily.…”

Fake News as We Feel It: Perception and Conceptualization of the Term “Fake News” in the Media

“…Leetaru [15] proposes a somewhat complementary approach that he calls culturomics 2.0, which uses historical news data instead of books and can, according to the author, "yield intriguing new understandings of human society". In the same vein, Flaounas et al analyze the European mediasphere [11] and the writing style, gender bias and the popularity of particular topics [10] in large corpora of news articles. Landsall-Welfare et al [14], also using a large dataset of media reports, observe a change of framing and sentiment associated with nuclear power after the Fukushima nuclear disaster.…”