Does war deepen gender inequalities in politicians’ behavior or help erase them? We draw from the terror management theory developed in psychology to argue that the onset of a violent conflict is likely to push politicians to conform more strongly with traditional gender stereotypes because it helps individuals cope with existential fears. To test our argument, we use data on Ukrainian politicians’ engagement on social media (136,455 Facebook posts by 469 politicians) in the three months before and after the 2022 Russian invasion of Ukraine, and interrupted time series analysis, to assess the effect of conflict on politicians’ behavior. We find that conflict onset deepens gender-stereotypical behavior among politicians in their public engagement. We also show that, consistent with our argument, gender biases among the public are magnified during war.

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.

What is the relationship between policy positions taken in campaigns and those proposed in bargaining when the final policy outcome depends on other political actors? Why do candidates sometimes advocate policies in their campaigns that are unlikely or impossible to pass given the preferences of other actors in the government? We analyze a model in which candidates make non-binding policy platform announcements and then bargain with a veto player over the final policy if they take office. In the model, a candidate has private information that is related to the policy preferences of a key citizen group and engages in bargaining with a veto player who is responsive to this information. When the citizen's group sometimes interprets campaign promises naively, elections are more likely to allow information revelation. Furthermore, in this case, politicians overpromise: the politician's platform is outside of the range of feasible bargaining outcomes.

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.

Political polarization has become a ubiquitous feature of American politics, exacerbated by the rise of online news and social media. While existing research has documented how polarization manifests online, relatively few studies have considered whether these divisions extend to discussions of foreign policy. We examine this question by analyzing nearly 2 million tweets about the war in Ukraine posted by Americans during the opening stages of the Russian invasion. We first categorize each tweet according to the user's ideological leanings estimated by the network of political accounts they follow. Then, we apply a natural language processing model specifically designed for short texts to classify the tweets into clusters that we hand code into substantive topics. We find that the topic distributions of conservative, moderate, and liberal users are substantively and statistically different. We further find that conservatives are more likely to spread some form of misinformation and that liberals are more likely to express support for Ukraine.