Explore the vast range of titles available.

There is some evidence that liberal politicians use more complex language than conservative politicians. This evidence, however, is based on a specific set of speeches of US members of Congress and UK members of Parliament. This raises the question whether the relationship between ideology and linguistic complexity is a more general phenomenon or specific to this small group of politicians. To address this question, this paper analyzes 381,609 speeches given by politicians from five parliaments, by twelve European prime ministers, as well as speeches from party congresses over time and across countries. Our results replicate and generalize earlier findings: speakers from culturally liberal parties use more complex language than speakers from culturally conservative parties. Economic left-right differences, on the other hand, are not systematically linked to linguistic complexity.

Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.

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.

Recent empirical workin the study of political sophistication finds that citizens’ knowledge of politics is not only a function of their individual characteristics but also depends on the supply of information from their environment (the ‘information environment’). Yet this literature does not address the question of how the information environment may be shaped by institutional factors. This article aims to fill this void. It first argues that the relationship between a government and the media affects the information that is available to individual citizens. Using cross-national data, it then finds that less government interference with the media (1) positively affects political learning and (2) moderates the individual-level effect of education on learning.

Objective Democratic governance requires that policy outcomes and public demand for policy be linked. While studies have shown empirical support for such a relationship in various policy domains, empirical evidence also indicates that the public is relatively unaware of policy outputs. This raises a puzzle: Why do policy outputs influence public attitudes if the public knows little about them? Methods This study seeks to address this paradox by examining the conditioning role of media coverage. We rely on data derived from the Policy Agendas Project in the United States, allowing us to analyze the relationship between policy outcomes, public preferences, and newspaper content across a long span of time (1972–2007). Results Our results indicate that public policy preferences respond to policy outputs, and that this relationship is strengthened by greater media attention to a policy area. Importantly, our findings also indicate that without media attention to a policy area, there is no direct effect of policy outputs on public demand for policy. Conclusions Media coverage appears to be a key factor for public responsiveness to occur. In the absence of policy coverage by the media, public responsiveness to policy outputs is greatly reduced.

This study investigates researcher variability in computational reproduction, an activity for which it is least expected. Eighty-five independent teams attempted numerical replication of results from an original study of policy preferences and immigration. Reproduction teams were randomly grouped into a ‘transparent group’ receiving original study and code or ‘opaque group’ receiving only a method and results description and no code. The transparent group mostly verified original results (95.7% same sign and p -value cutoff), while the opaque group had less success (89.3%). Second-decimal place exact numerical reproductions were less common (76.9 and 48.1%). Qualitative investigation of the workflows revealed many causes of error, including mistakes and procedural variations. When curating mistakes, we still find that only the transparent group was reliably successful. Our findings imply a need for transparency, but also more. Institutional checks and less subjective difficulty for researchers ‘doing reproduction’ would help, implying a need for better training. We also urge increased awareness of complexity in the research process and in ‘push button’ replications.

Scholars often study isolated media effects using one method at one time point in one country. We seek to generalise the research in this area by examining hundreds of press-worthy events across dozens of countries at various points in time with an array of techniques and outcome measures. In particular, we merge a database containing thousands of events with five waves of the European Social Survey to conduct analyses across countries and individuals as well as within countries and for specific respondents. The results suggest that there is an impressive degree of heterogeneity when it comes to how citizens react to political developments. Some events generate significant opinion changes when groups of individuals who are ‘treated’ are compared with ‘control’ cases. However, other events produce modest or even null findings with methods that employ different counterfactuals. Thus, findings of both strong and weak media effects that scholars have uncovered over the years could be a function of methodological choices as well as context-specific factors such as institutional arrangements, media systems, eras, or event characteristics. Data limitations also make some research designs possible while they preclude others. We conclude with advice for others who wish to study political events in this manner as well as discussion of media effects, broadly construed.