Social media giants stand accused of facilitating illegitimate interference with democratic political processes around the world. Part of this problem are malicious bots: automated fake accounts passing as humans. However, we lack a systematic understanding of which politicians benefit most from them. We tackle this question by leveraging a Twitter purge of malicious bots in July 2018 and a new dataset on Twitter activity by all members of national parliaments (MPs) in the EU in 2018. Since users had no influence on how and when Twitter purged millions of bots, it serves as an exogenous intervention to investigate whether some parties or politicians lost more followers. We find drops in follower counts concentrated among radical right politicians, in particular those with strong anti-EU discourse. This is the first set of empirical, causally identified evidence supporting the idea that the radical right benefits more from malicious bots than other party families.

Depression is the most common mental health disorder. It has consequences not only on individuals but also on social and political levels. We argue that depressive symptoms impair political participation by reducing the motivation and physical energy of sufferers. We test our hypotheses by conducting regression analyses of four nationally representative cross-sectional and longitudinal surveys that collectively span many democracies. Our results are threefold. First, we find that the severest depressive symptoms lower the probability of voting by 0.05–0.25 points, an effect that is exceeded only by education and age. Second, we show that depressive symptoms negatively affect political interest and internal efficacy, thereby confirming that they diminish political motivation. Third, we find that depressive symptoms most strongly affect physically demanding acts, thereby confirming that they reduce the physical energy required for participation. We conclude by urging scholars to take depressive symptoms seriously in the study of political behavior.

Despite extensive research on international norms, our approach to measurement has not kept pace with theoretical advancements. Existing research often relies on single indicators to facilitate cross-national analysis or employs case-study designs that provide greater nuance but restricted scope. Given these limitations, this note argues that item-response theory (IRT) provides a framework for strengthening the link between our theoretical understanding of norms and empirical measurement of norm adoption. In turn, I develop a modified Bayesian model with substantively informed dynamic priors. The proposed approach is evaluated with the lesbian, gay, and bisexual (LGB) equality norm, using 13 policies and laws across 196 countries (1990–2017). The results are broadly consistent with theoretical expectations while also providing new empirical evidence on the evolution of the norm across space and time. This note highlights the significant potential in greater interaction between both latent measurement approaches and scholarship on international norms.

Identifying important policy outputs has long been of interest to political scientists. In this work, we propose a novel approach to the classification of policies. Instead of obtaining and aggregating expert evaluations of significance for a finite set of policy outputs, we use experts to identify a small set of significant outputs and then employ positive unlabeled (PU) learning to search for other similar examples in a large unlabeled set. We further propose to automate the first step by harvesting “seed” sets of significant outputs from web data. We offer an application of the new approach by classifying over 9,000 government regulations in the United Kingdom. The obtained estimates are successfully validated against human experts, by forecasting web citations, and with a construct validity test.