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The rise of populism concerns many political scientists and practitioners,yet the detection of its underlying language remains fragmentary. This paperaims to provide a reliable, valid, and scalable approach to measure populiststances. For that purpose, we created an annotated dataset based onparliamentary speeches of the German Bundestag (2013 to 2021). Following theideational definition of populism, we label moralizing references to thevirtuous people or the corrupt elite as core dimensions of populist language.To identify, in addition, how the thin ideology of populism is thickened, weannotate how populist statements are attached to left-wing or right-wing hostideologies. We then train a transformer-based model (PopBERT) as a multilabelclassifier to detect and quantify each dimension. A battery of validationchecks reveals that the model has a strong predictive accuracy, provides highqualitative face validity, matches party rankings of expert surveys, anddetects out-of-sample text snippets correctly. PopBERT enables dynamic analysesof how German-speaking politicians and parties use populist language as astrategic device. Furthermore, the annotator-level data may also be applied incross-domain applications or to develop related classifiers.

PopBERT. Detecting populism and its host ideologies in the German Bundestag