This paper investigates the identification of populist rhetoric in text and presents a novel cross-lingual dataset for this task. Our work is based on the definition of populism as a "communication style of political actors that refers to the people" but also includes anti-elitism as another core feature of populism. Accordingly, we annotate references to people and elite in German and English parliamentary debates with a hierarchical scheme. The paper describes our dataset and annotation procedure and reports inter-annotator agreement for this task. Next, we compare and evaluate different transformer-based model architectures on a German dataset and report results for zero-shot learning on a smaller English data. We then show that semi-supervised tri-training can improve results in the cross-lingual setting. Our dataset can be used to investigate how political actors talk about elite and people and to study how populist rhetoric is used as a strategic device.

Our kind of people? Detecting populist references in political debates

This paper investigates the identification of populist rhetoric in text and presents a novel cross-lingual dataset for this task. Our work is based on the definition of populism as a "communication style of political actors that refers to the people" but also includes anti-elitism as another core feature of populism. Accordingly, we annotate references to The People and The Elite in German and English parliamentary debates with a hierarchical scheme. The paper describes our dataset and annotation procedure and reports inter-annotator agreement for this task. Next, we compare and evaluate different transformer-based model architectures on a German dataset and report results for zero-shot learning on a smaller English dataset. We then show that semi-supervised tri-training can improve results in the cross-lingual setting. Our dataset can be used to investigate how political actors talk about The Elite and The People and to study how populist rhetoric is used as a strategic device.

**Organizers:** Stefania Degaetano-Ortlieb, Department of Language Science and Technology, Universität des Saarlandes; Anna Kazantseva, National Research Council Canada; Nils Reiter, Institut für Digital Humanities, University of Cologne; Stan Szpakowicz, School of Electrical Engineering and Computer Science, University of Ottawa For more details, please visit our [workshop website](https://sighum.wordpress.com/events/latech-clfl-2023/))

7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities, and Literature

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Our kind of people? Detecting populist references in political debates

Our kind of people? Detecting populist references in political debates

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