In this paper, we analyze a combination of social and semantic networks extracted from news media articles on the recent European debt crisis. While most network research, so far, has focused on either social networks or semantic networks, we combine networks of entities (i.e., organizations and actors) and semantic networks to identify the discursive frames used to characterize these different entities. The use of socio-semantic networks in the analysis of discursive fames is a novel approach in network research.
Media reporting of the financial crisis in the United States has been widely investigated by communication researchers, but the same does not apply to the European debt crisis. Since the end of 2009, several Eurozone member states were unable to repay or refinance their government debt or to bailout over-indebted banks under their national supervision. Spillover effects of the financial situation in the United States and the rising sovereign debt of several Eurozone states contributed to high financial instability in Europe. In reporting these events of the Eurozone debt crisis, the media makes use of frames to make their reports more prominent and/or popularize the issues discusses. The importance of frames in media reports stems from their ability to highlight some aspects of a perceived reality and make them more salient, while at the same time hiding other aspects. By employing frames to characterize or describe the financial situation of the Eurozone, media reports have the potential to influence perceptions surrounding the crisis, to motivate action, or to potentially create panic among their audiences.Using semantic network analysis, the structural space approach, and parts of speech tagging, we identify different types of frames on a monthly basis in 1105 news items published by The Financial Times between October 2009 and October 2010. We show the over time evolution of frames used by The Financial Times to characterize organizations and actors (i.e., entities) in the debate relevant to the financial crisis. Our results show that the debate was only framed as a ‘crisis’ from January 2010 onwards, and that the similarity of correlations between our semantic networks are in a large part driven by shared concepts, but they are also driven by the structure of these networks beyond the common nodes. This means that both the content and the structure of the frame networks changes during the crisis. Our work contributes to socio-semantic network analysis by revealing the context in which entities are framed and blamed in relation the Eurocrisis.
Presented at Sunbelt 2016
In : Abstracts