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        <title>Our kind of people? Detecting populist references in political debates</title>
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        <namePart type="given">Christopher</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.91</identifier>
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%0 Conference Proceedings
%T Our kind of people? Detecting populist references in political debates
%A Klamm, Christopher
%A Rehbein, Ines
%A Ponzetto, Simone Paolo
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F klamm-etal-2023-kind
%X 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.
%R 10.18653/v1/2023.findings-eacl.91
%U https://aclanthology.org/2023.findings-eacl.91
%U https://doi.org/10.18653/v1/2023.findings-eacl.91
%P 1227-1243

Markdown (Informal)

[Our kind of people? Detecting populist references in political debates](https://aclanthology.org/2023.findings-eacl.91) (Klamm et al., Findings 2023)

• Our kind of people? Detecting populist references in political debates (Klamm et al., Findings 2023)

ACL

• Christopher Klamm, Ines Rehbein, and Simone Paolo Ponzetto. 2023. Our kind of people? Detecting populist references in political debates. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1227–1243, Dubrovnik, Croatia. Association for Computational Linguistics.