Search

Automated text analysis allows researchers to analyze large quantities of text. Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. To address this issue, some analysts have suggested using Google Translate to convert all texts into English before starting the analysis (Lucas et al. 2015). But in doing so, do we get lost in translation? This paper evaluates the usefulness of machine translation for bag-of-words models—such as topic models. We use the europarl dataset and compare term-document matrices (TDMs) as well as topic model results from gold standard translated text and machine-translated text. We evaluate results at both the document and the corpus level. We first find TDMs for both text corpora to be highly similar, with minor differences across languages. What is more, we find considerable overlap in the set of features generated from human-translated and machine-translated texts. With regard to LDA topic models, we find topical prevalence and topical content to be highly similar with again only small differences across languages. We conclude that Google Translate is a useful tool for comparative researchers when using bag-of-words text models.

Media effects research has produced mixed findings about the size and direction of the relationship between media consumption and public attitudes (e.g., Bartels, 1993). 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 2005-10 panel data. We compare the direction and size of effects of media content on attitude change across: 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 analyze 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.

The thermostatic theory of representation posits that public policy preferences are driven, in part, by the policy actions of governments. That is, when the government provides the public with more policy in a given domain than it desires, public demand in that domain will decrease, and when the government provides less policy than the public desires, public demand for policy in that domain will increase. A large amount of literature has shown empirical support for this theory in various policy domains. However, there is also empirical evidence to indicate that the public pays little attention to policy outputs. Thus, the question becomes, if the public is largely unaware of policy output, why does it influence public attitudes? This paper seeks to address this paradox by examining the relationship between change in public policy preferences, policy output, and media attention in a given policy area. To conduct this study, we rely upon data derived from the Policy Agendas Project in the United States, as well as public opinion data measuring the public’s relative preferences for spending across multiple policy domains. We find support for the basic premise that policy outputs influence public policy preferences. More interestingly, however, we also find that this relationship is conditioned on the degree of media attention a particular policy area garners. Our results shed new light on the boundary conditions of public responsiveness to policy outcomes, and have important implications for our understanding of representative democracy.

In this paper we investigate prime ministers’ communication strategies during the most re- cent economic crisis in Europe. We argue that when electoral risk is high but governments’ policy options are severely limited, prime ministers will use specific communication strategies to mitigate electoral risks. We analyze two such communication strategies – blame avoidance and blame-shifting – by applying state-of-the-art quantitative text analysis meth- ods on 5402 speeches of prime ministers in 9 EU member states. We find evidence for both strategies. Prime ministers talk about the economy more in response to both high (domestic) unemployment and low (domestic) GDP growth. Furthermore, we find the (domestic) unemployment rate to be the most consistent predictor of blame-shifting: as the domestic unemployment rate goes up, this is followed by an increase in blame-shifting towards banks, Germany, Greece and the Troika.

This paper presents an agent-based model (ABM) that shows that variation in voter knowledge can in part be driven by competition among media outlets. Using a set of simple behavioral rules implemented by voters, parties and media outlets, the model predicts that stronger media competition increases political knowledge of quality-minded voters vis-a-vis motivated reasoners although aggregate differences are small. This is because these voters are most likely to consume news even when it is of low quality. Not only do these results contribute to a larger (empirical) debate about media competition and political knowledge, but the model also serves as a theoretical starting point for exploring these patterns further.

In this paper we investigate prime ministers’ communication strategies during the most re- cent economic crisis in Europe. We argue that when electoral risk is high but governments’ policy options are severely limited, prime ministers will use specific communication strategies to mitigate electoral risks. We analyze two such communication strategies – blame avoidance and blame-shifting – by applying state-of-the-art quantitative text analysis meth- ods on 5402 speeches of prime ministers in 9 EU member states. We find evidence for both strategies. Prime ministers talk about the economy more in response to both high (domestic) unemployment and low (domestic) GDP growth. Furthermore, we find the (domestic) unemployment rate to be the most consistent predictor of blame-shifting: as the domestic unemployment rate goes up, this is followed by an increase in blame-shifting towards banks, Germany, Greece and the Troika.