Optimizing text representations to capture (dis)similarity between political parties

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 325 - 338 December 7-8, 2022 ©2022 Association for Computational Linguistics Optimizing text representations to capture (dis)similarity between political parties Tanise Ceron△ Nico Blokker2 Sebastian Padó△ △ Institute for Natural Language Processing, University of Stuttgart, Germany 2 Research Center on Inequality and Social Policy, University of Bremen, Germany {tanise.ceron,pado}@ims.uni-stuttgart.de, blokker@uni-bremen.de Abstract Even though fine-tuned neural language models have been pivotal in enabling “deep” automatic text analysis, optimizing text representations for specific applications remains a crucial bot- tleneck. In this study, we look at this problem in the context of a task from computational social science, namely modeling pairwise simi- larities between political parties. Our research question is what level of structural information is necessary to create robust text representa- tion, contrasting a strongly informed approach (which uses both claim span and claim cate- gory annotations) with approaches that forgo one or both types of annotation with document structure-based heuristics. Evaluating our mod- els on the manifestos of German parties for the 2021 federal election. We find that heuristics that maximize within-party over between-party similarity along with a normalization step lead to reliable party similarity prediction, without the need for manual annotation. 1 Introduction A party manifesto, also known as electoral program, is a document in which parties express their views, intentions and motives for the next coming years. Since this genre of text is written not just to inform, but to persuade potential voters that the parties compete for (Budge et al., 2001), it provides a strong basis to understand the position taken by parties according to various policies because of its direct access to the parties’ opinions. Political scientists study the contents of party manifestos, for instance, to investigate parties’ similarity with respect to the several policies (Budge, 2003), to predict party coalitions (Druckman et al., 2005), and to evaluate the extent to which the parties that they vote for actually corresponds to their own world view (McGregor, 2013). To carry out systematic analyses of party rela- tions while taking into account differences in style and level of detail, these analyses are increasingly grounded in two types of manual annotation about claims, statements that contain a position or a view towards an issue, that can be argued or demanded for (Koopmans and Statham, 1999): First, abstract claim categories (Burst et al., 2021) are used to group together diverse forms and formulations of demands. Second, annotation often includes the stance that parties take towards specific political claims to abstract away from the many ways to ex- press support or rejection in language. In addition, these types of annotation offer a direct way to em- pirically ground party similarity in claims and link these to concrete textual statements. At the same time, such manual annotation is extremely expen- sive in terms of time and resources and has to be repeated for every country and every new election. In this paper, we investigate the extent to which this manual effort …

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