For now more than four decades, quantitative protest event analysis (PEA) has routinely contributed to the testing and refinement of theories on political processes from different perspectives. However, it is commonly agreed that PEA data face serious challenges regarding their data sources. Precisely, researchers applying PEA struggle with the fact that they cannot use multiple sources for large geographical areas and long time periods. As a consequence, most of the scholarship still focuses on a narrow set of European countries or the United States and does not cover the period since the early 2000s. We are bringing PEA and computational linguistics together to suggest and evaluate an approach that will enable political scientists to extend their research designs with a more efficient and at the same time reliable data collection. The approach relies on hidden topic models, word space models, and named entity recognition to identify and code protest events.
Calls for more generalizable empirical examinations rank among the top priorities of discursive institutionalists. However, there are hardly any approaches that address the specific challenges of such examinations with regard to the systematic comparison of public discourses across countries. This contribution first develops a methodological framework for a comprehensive study of public discourse and subsequently applies it to study labour market policy discourse in six Western European countries from 2004 until 2006. Subsequently, the frame analysis shows that ideas brought forward in these public discourses relate to the three major concepts identified by the comparative political economy literature: corporatism; neoliberalism; and compensation. Furthermore, the findings corroborate the expectations derived from the discursive institutionalist literature, since the salience of the frames does systematically vary according to the institutional legacies of the countries, as well as to the interests of the actors involved. Subsequently, the frame analysis shows that ideas brought forward in these public discourses relate to the three major concepts identified by the comparative political economy literature: corporatism, neoliberalism and compensation. Furthermore, the findings corroborate the expectations derived from the discursive institutionalist literature, since the salience of the frames does systematically vary according to the institutional legacies of the countries as well as to the interests of the actors involved.
Widely renowned typologies in Comparative Political Economy like the 'Varieties of Capitalism' or the 'Three Worlds of Welfare Capitalism' are criticized to neglect political conflict, because they selectively focus on institutional characteristics, most notably labor relations and welfare regimes. In doing so, they fall short to grasp the whole meaning of their categories. This analysis moves beyond institutionally defined political-economic arrangements and studies the role of public debates for different capitalist models. Using novel relational data from an extensive content analysis of newspapers from 2004 to 2006, political conflicts on economic liberalization in Britain, France and Germany are explored. More specifically, the structure of conflicts and the influence of various political actors for the debate on economic liberalization are precisely assessed. The results reveal persistent national peculiarities with respect to political contention which can plausibly be ascribed to the influence of long-term historical legacies and institutional complementariness as outlined by capitalist typologies.
Among the many applications in social science for the entry and management of data, there are only a few software packages that apply natural language processing to identify semantic concepts such as issue categories or political statements by actors. Although these procedures usually allow efficient data collection, most have difficulty in achieving sufficient accuracy because of the high complexity and mutual relationships of the variables used in the social sciences. To address these flaws, we suggest a (semi-) automatic annotation approach that implements an innovative coding method (Core Sentence Analysis) by computational linguistic techniques (mainly entity recognition, concept identification, and dependency parsing). Although such computational linguistic tools have been readily available for quite a long time, social scientists have made astonishingly little use of them. The principal aim of this article is to gather data on party-issue relationships from newspaper articles. In the first stage, we try to recognize relations between parties and issues with a fully automated system. This recognition is extensively tested against manually annotated data of the coverage in the boulevard newspaper
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