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We investigate whether election results are associated with emotional reactions among voters across democracies and under what conditions these responses are more intense. Building on recent work in comparative politics, we theorize that emotional intensity is stronger after elections involving populist candidates and highly polarized parties. We test these expectations with a big-data analysis of emotional reactions on parties’ Facebook posts during 29 presidential elections in 26 democracies. The results show that ideological polarization of political parties might intensify emotional reactions, but there is no clear relationship with the presence of populist candidates. 

Abstract Which parties embrace multilingualism in their communication? Despite growing interest in parties’ multilingualism among normative scholars of deliberative democracy, empirical research has largely overlooked the linguistic aspect of party competition. We leverage large‐scale data on Facebook posts by more than 800 parties in 87 democracies and analyze their day‐to‐day language practices. By so doing, we develop, for the first time, the classification of monolingual and multilingual parties around the world. Moreover, using this novel dataset, we explore what factors are associated with parties’ adoption of multilingualism and how multilingual parties predict the language use of candidates they nominate. Overall, this study provides the most comprehensive picture of parties’ multilingualism in contemporary democracies and sets agendas for future research in the intersection of parties and language representation. 

Abstract We present a hierarchical Dirichlet regression model with Gaussian process priors that enables accurate and well-calibrated forecasts for U.S. Senate elections at varying time horizons. This Bayesian model provides a balance between predictions based on time-dependent opinion polls and those made based on fundamentals. It also provides uncertainty estimates that arise naturally from historical data on elections and polls. Experiments show that our model is highly accurate and has a well calibrated coverage rate for vote share predictions at various forecasting horizons. We validate the model with a retrospective forecast of the 2018 cycle as well as a true out-of-sample forecast for 2020. We show that our approach achieves state-of-the art accuracy and coverage despite relying on few covariates. 

Abstract Topic models, as developed in computer science, are effective tools for exploring and summarizing large document collections. When applied in social science research, however, they are commonly used for measurement, a task that requires careful validation to ensure that the model outputs actually capture the desired concept of interest. In this paper, we review current practices for topic validation in the field and show that extensive model validation is increasingly rare, or at least not systematically reported in papers and appendices. To supplement current practices, we refine an existing crowd-sourcing method by Chang and coauthors for validating topic quality and go on to create new procedures for validating conceptual labels provided by the researcher. We illustrate our method with an analysis of Facebook posts by U.S. Senators and provide software and guidance for researchers wishing to validate their own topic models. While tailored, case-specific validation exercises will always be best, we aim to improve standard practices by providing a general-purpose tool to validate topics as measures. 

The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response funcitons (IRFs) that map from latent trait to observed response. However, in many cases observed behavior can deviate significantly from the parametric assumptions of traditional IRT models. Nonparametric IRT (NIRT) models overcome these challenges by relaxing assumptions about the form of the IRFs, but standard tools are unable to simultaneously estimate flexible IRFs and recover ability estimates for respondents. We propose a Bayesian nonparametric model that solves this problem by placing Gaussian process priors on the latent functions defining the IRFs. This allows us to simultaneously relax assumptions about the shape of the IRFs while preserving the ability to estimate latent traits. This in turn allows us to easily extend the model to further tasks such as active learning. GPIRT therefore provides a simple and intuitive solution to several longstanding problems in the IRT literature.