The European NUTS-level election dataset: A tool to map European electoral geography.

Datasets on subnational election results in Europe frequently do not match with regional statistics available for cross-national research, mainly because territorial statistical units change over time and do not map onto the national electoral districts. This hinders consistent comparative research across time. This research note introduces EU-NED, a new dataset on subnational election data that covers national and European parliamentary elections for European countries over the past 30 years. EU-NED's major contribution is that it provides election results on disaggregated levels of the statistical territorial units used by Eurostat with an unprecedented consistency and temporospatial scope. Moreover, EU-NED is integrated with the Party Facts platform, allowing for a seamless integration of party-level data. Using EU-NED, we present first descriptive evidence on the European electoral geography and suggest avenues of how EU-NED can facilitate future comparative political science research in Europe.

Empathy-based counterspeech can reduce racist hate speech in a social media field experiment.

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation-either by governments or social media companies-can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies-empathy, warning of consequences, and humor-or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.