Towards a More In-Depth Detection of Political Framing

Qi Yu


Abstract
In social sciences, recent years have witnessed a growing interest in applying NLP approaches to automatically detect framing in political discourse. However, most NLP studies by now focus heavily on framing effect arising from topic coverage, whereas framing effect arising from subtle usage of linguistic devices remains understudied. In a collaboration with political science researchers, we intend to investigate framing strategies in German newspaper articles on the “European Refugee Crisis”. With the goal of a more in-depth framing analysis, we not only incorporate lexical cues for shallow topic-related framing, but also propose and operationalize a variety of framing-relevant semantic and pragmatic devices, which are theoretically derived from linguistics and political science research. We demonstrate the influential role of these linguistic devices with a large-scale quantitative analysis, bringing novel insights into the linguistic properties of framing.
Anthology ID:
2023.latechclfl-1.18
Volume:
Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Stefania Degaetano-Ortlieb, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
Venue:
LaTeCHCLfL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
162–174
Language:
URL:
https://aclanthology.org/2023.latechclfl-1.18
DOI:
10.18653/v1/2023.latechclfl-1.18
Bibkey:
Cite (ACL):
Qi Yu. 2023. Towards a More In-Depth Detection of Political Framing. In Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 162–174, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Towards a More In-Depth Detection of Political Framing (Yu, LaTeCHCLfL 2023)
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PDF:
https://aclanthology.org/2023.latechclfl-1.18.pdf
Video:
 https://aclanthology.org/2023.latechclfl-1.18.mp4