Additive manifesto decomposition: A policy domain aware method for understanding party positioning

Tanise Ceron, Dmitry Nikolaev, Sebastian Padó

Abstract
Automatic extraction of party (dis)similarities from texts such as party election manifestos or parliamentary speeches plays an increasing role in computational political science. However, existing approaches are fundamentally limited to targeting only global party (dis)-similarity: they condense the relationship between a pair of parties into a single figure, their similarity. In aggregating over all policy domains (e.g., health or foreign policy), they do not provide any qualitative insights into which domains parties agree or disagree on. This paper proposes a workflow for estimating policy domain aware party similarity that overcomes this limitation. The workflow covers (a) definition of suitable policy domains; (b) automatic labeling of domains, if no manual labels are available; (c) computation of domain-level similarities and aggregation at a global level; (d) extraction of interpretable party positions on major policy axes via multidimensional scaling. We evaluate our workflow on manifestos from the German federal elections. We find that our method (a) yields high correlation when predicting party similarity at a global level and (b) provides accurate party-specific positions, even with automatically labelled policy domains.
Anthology ID:
2023.findings-acl.499
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7874–7890
Language:
URL:
https://aclanthology.org/2023.findings-acl.499
DOI:
10.18653/v1/2023.findings-acl.499
Bibkey:
Cite (ACL):
Tanise Ceron, Dmitry Nikolaev, and Sebastian Padó. 2023. Additive manifesto decomposition: A policy domain aware method for understanding party positioning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7874–7890, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Additive manifesto decomposition: A policy domain aware method for understanding party positioning (Ceron et al., Findings 2023)
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