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European citizens' attitudes towards defence are a fundamental element of common identity in the EU's aim of becoming a unitary international actor. In Spain, since the early 2000s, the Spanish Ministry of Defence and the Armed Forces have developed a public policy to promote defence culture to influence Spanish society's views, perceptions, opinions, etc., towards defence and military policies. However, this approach is flawed because, despite being defined as part of the Spanish citizens' political culture, these institutions reduce this concept to 'knowledge' or 'awareness of its importance'. Therefore, the understanding of the Spanish defence political culture has been ill-analysed. Hence, a political culture related to defence issues can be best explained within the political culture framework, from which an ideal type, in the Weberian sense, can be established. Thus, based on previous studies, using survey data and both exploratory and confirmatory factor analysis (EFA & CFA), we test five sub-models to explain this political culture. This research is relevant to the study of the defence political culture because it reaffirms the existence of different cultural subtypes and civil–military relations and shows that the cultural gap in the convergence of values must be worked on in a segmented manner. [ABSTRACT FROM AUTHOR]

To locate media frames is one of the biggest challenges facing academics in Political Communication disciplines. The traditional approach to the problem is the use of different coders and their subsequent comparison, either through statistical analysis, or through agreements between them. In both cases, problems arise due to the difficulty of defining exactly where the frame is as well as its meaning and implications. And, above all, it is a complex process that makes it very difficult to work with large data sets. The authors, however, propose the use of information cataloging algorithms as a way to solve these problems. These algorithms (Support Vector Machines, Random Forest, CNN, etc.) come from disciplines linked to neural networks and have become an industry standard devoted to the treatment of non-numerical information and natural language processing. In addition, when supervised, they can be trained to find the information that the researcher considers pertinent. The authors present one case study, the media framing of the refugee crisis in Europe (in 2015) as an example. In that regard, SVM shows a lot of potential, being able to locate frames successfully albeit with some limitations. [ABSTRACT FROM AUTHOR]