Automatic extraction of party (dis)similarities from texts such as party election manifestos or parliamentary speeches plays an increasing role in computational political science. How- ever, existing approaches are fundamentally limited to targeting only global party (dis)- similarity: they condense the relationship be- tween a pair of parties into a single figure, their similarity. In aggregating over all policy do- mains (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 estimat- ing policy domain aware party similarity that overcomes this limitation. The workflow cov- ers (a) definition of suitable policy domains; (b) automatic labeling of domains, if no man- ual 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 mul- tidimensional scaling. We evaluate our work- flow on manifestos from the German federal elections. We find that our method (a) yields high correlation when predicting party similar- ity at a global level and (b) provides accurate party-specific positions, even with automati- cally labelled policy domains.
%0 Conference Paper
%1 ceron23:_addit
%A Ceron, Tanise
%A Nikolaev, Dmitry
%A Padó, Sebastian
%B Findings of ACL
%C Toronto, Canada
%D 2023
%K conference myown
%T Additive manifesto decomposition: A policy domain aware method for understanding party positioning
%U https://aclanthology.org/2023.findings-acl.499/
%X Automatic extraction of party (dis)similarities from texts such as party election manifestos or parliamentary speeches plays an increasing role in computational political science. How- ever, existing approaches are fundamentally limited to targeting only global party (dis)- similarity: they condense the relationship be- tween a pair of parties into a single figure, their similarity. In aggregating over all policy do- mains (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 estimat- ing policy domain aware party similarity that overcomes this limitation. The workflow cov- ers (a) definition of suitable policy domains; (b) automatic labeling of domains, if no man- ual 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 mul- tidimensional scaling. We evaluate our work- flow on manifestos from the German federal elections. We find that our method (a) yields high correlation when predicting party similar- ity at a global level and (b) provides accurate party-specific positions, even with automati- cally labelled policy domains.
@inproceedings{ceron23:_addit,
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. How- ever, existing approaches are fundamentally limited to targeting only global party (dis)- similarity: they condense the relationship be- tween a pair of parties into a single figure, their similarity. In aggregating over all policy do- mains (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 estimat- ing policy domain aware party similarity that overcomes this limitation. The workflow cov- ers (a) definition of suitable policy domains; (b) automatic labeling of domains, if no man- ual 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 mul- tidimensional scaling. We evaluate our work- flow on manifestos from the German federal elections. We find that our method (a) yields high correlation when predicting party similar- ity at a global level and (b) provides accurate party-specific positions, even with automati- cally labelled policy domains.},
added-at = {2023-05-02T15:14:08.000+0200},
address = {Toronto, Canada},
author = {Ceron, Tanise and Nikolaev, Dmitry and Padó, Sebastian},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c802a4bcaa362732985cfbe6b4ab376e/sp},
booktitle = {Findings of ACL},
interhash = {c15728109a92b2c1f84c603ca1c5249b},
intrahash = {c802a4bcaa362732985cfbe6b4ab376e},
keywords = {conference myown},
timestamp = {2024-02-22T12:31:33.000+0100},
title = {Additive manifesto decomposition: {A} policy domain aware method for understanding party positioning},
url = {https://aclanthology.org/2023.findings-acl.499/},
year = 2023
}
