The Structure of the EU Mediasphere

Background. 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. Methodology/Principal Findings. 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. Conclusions/Significance. 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

Journalism as usual: The use of social media as a newsgathering tool in the coverage of the Iranian elections in 2009

The Iranian elections of June 2009 and the ensuing protests were hailed as the 'Twitter revolution' in the media in the United Kingdom. However, this study of the use of sources by journalists covering the events shows that despite their rhetoric of the importance of social media in alerting the global community to events in Iran, journalists themselves did not turn to that social media for their own information, but relied most on traditional sourcing practices: political statements, expert opinion and a handful of 'man on the street' quotes for colour. This study shows that although the mythology of the Internet as a place where all voices are equal, and have equal access to the public discourse continues – a kind of idealized 'public sphere' – the sourcing practices of journalists and the traditions of coverage continue to ensure that traditional voices and sources are heard above the crowd

Web Searching: A Quality Measurement Perspective

The purpose of this paper is to describe various quality measures for search engines and to ask whether these are suitable. We especially focus on user needs and their use of web search engines. The paper presents an extensive literature review and a first quality measurement model, as well. Findings include that search engine quality can not be measured by just retrieval effectiveness (the quality of the results), but should also consider index quality, the quality of the search features and search engine usability. For each of these sections, empirical results from studies conducted in the past, as well as from our own research are presented. These results have implications for the evaluation of search engines and for the development of better search systems that give the user the best possible search experience