Our kind of people? Detecting populist references in political debates

Christopher Klamm, Ines Rehbein, Simone Paolo Ponzetto


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.
Anthology ID:
2023.findings-eacl.91
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1227–1243
Language:
URL:
https://aclanthology.org/2023.findings-eacl.91
DOI:
10.18653/v1/2023.findings-eacl.91
Bibkey:
Cite (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.
Cite (Informal):
Our kind of people? Detecting populist references in political debates (Klamm et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.91.pdf
Dataset:
 2023.findings-eacl.91.dataset.zip
Video:
 https://aclanthology.org/2023.findings-eacl.91.mp4