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In this paper, we examine how and why the European Commission's ideas on fiscal policy have changed over the course of the European Semester. Empirically, we rely on a semi-supervised scaling approach to identify economic ideas as they appear in Semester documents from the aftermath of the financial crisis onwards (2011-2022). Our findings demonstrate a gradual shift in ideas from an ordoliberal to a Keynesian direction, especially during the overlapping Von der Leyen and Covid-19 crisis years. We identify substantial country-specific differences throughout 2011–2022, with some countries receiving exclusively Keynesian recommendations, and others distinctively ordoliberal ones. These patterns can be explained in part by economic conditions in those member states but not by the public's trust in the EU. These results underscore the reactive nature of the Commission's economic ideology; crises, as well as member states' economic conditions, shape the direction of its fiscal policy recommendations. [ABSTRACT FROM AUTHOR]

We identify three gaps that limit the utility and obstruct the progress of computational text analysis methods (CTAM) for social science research. First, we contend that CTAM development has prioritized technological over validity concerns, giving limited attention to the operationalization of social scientific measurements. Second, we identify a mismatch between CTAMs' focus on extracting specific contents and document-level patterns, and social science researchers' need for measuring multiple, often complex contents in the text. Third, we argue that the dominance of English language tools depresses comparative research and inclusivity toward scholarly communities examining languages other than English. We substantiate our claims by drawing upon a broad review of methodological work in the computational social sciences, as well as an inventory of leading research publications using quantitative textual analysis. Subsequently, we discuss implications of these three gaps for social scientists' uneven uptake of CTAM, as well as the field of computational social science text research as a whole. Finally, we propose a research agenda intended to bridge the identified gaps and improve the validity, utility, and inclusiveness of CTAM. [ABSTRACT FROM AUTHOR]

Media effects research has produced mixed findings about the size and direction of the relationship between media consumption and public attitudes. We investigate the extent to which model choices contribute to these inconsistent findings. Taking a comparative approach, we first review the use of different models in contemporary studies and their main findings. In order to extend and validate this review, we consider the implications for national election studies attempting to measure media effects in election campaigns and recreate these models with the British Election Study 2005–2010 panel data. We compare the direction and size of effects of media contenton attitude changeacross: between-subjects, within-elections models, in which the effects of individual-level variance in media exposure and content are assessed; within-subjects, within-elections models, which compare the effects of variance in media content for the same individual; and within-subjects, between-elections models that allow us to analyse the links between media content and exposure with attitude change over time. Our review shows some notable differences between models in terms of significance of effects (but not effect sizes). We corroborate this finding in the British campaign analysis. We conclude that to check the robustness of claims of media effects in observational data, where possible researchers should examine different model choices when evaluating media effects. [ABSTRACT FROM PUBLISHER]

Media exposure is one of the most important concepts in the social sciences, and yet scholars have struggled with how to operationalize it for decades. Some researchers have focused on the effects of variously worded self-report measures. Others advocate the use of aggregate and/or behavioral data that does not rely on a person’s ability to accurately recall exposure. Our study illustrates how an experimental design can be used to improve measures of exposure. In particular, we show how an experimental benchmark can be employed to (1) compare actual (i.e., manipulated) and self-reported values of news exposure; (2) assess how closely the self-reported items approximate the performance of “true” exposure in an empirical application; and (3) investigate whether a variation in question wording improves the accuracy of self-reported exposure measures. [ABSTRACT FROM PUBLISHER]