Additive manifesto decomposition: A policy domain aware method for understanding party positioning (2023)

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.

## introduction party competition is a fundamental process in democracies. it provides space for different political stances to emerge, allowing people to choose which of them they most identify with. investigating this process is relevant for understanding the reasons behind the choice of voters in elections as well as the behavior of parties in policy decisionmaking once in power @xcite . within political science, the positioning of parties is investigated under the umbrella term of "party competition". some studies look at specific policies such as "welcoming refugees", others, at broader domains such as "economy". traditionally, the positioning of parties within these policies or domains is scaled down to a reduced number of political dimensions such as the well-established left-right or the libertarian-authoritarian axes in order to facilitate the comparison among parties and their ideologies @xcite . analyses are usually carried out by experts, who gather policy and ideological stances of members of the political parties in several countries in europe and beyond @xcite . alternatively, electoral programs are manually annotated following a specific codebook that takes into account the position of the parties on policies so that the salience of the labels can be scrutinised @xcite . recently, computational approaches have been developed to automate and scale up party position analysis to larger amounts of text @xcite @xcite . this development has the potential of alleviating the burden of annotation, but has so far been realised only at an aggregated level: party positions are projected on the left-right scale or on a distance-based approach between party pairs according to several policies, not providing insights at the level of policy domains. this requires political scientists either to manually check for sections of the text of their interest in case the objective is to understand the positioning of parties on a more fine-grained level or to make assumptions about a policy considering the entire document. in this paper, we extend the previous studies to provide a computational model for party positions and party similarity at the level of policy domains. to do so, we semi-automatically decompose the texts into interpretable thematic blocks based on an updated inventory of annotated labels from the comparative manifesto project (cmp). sentence embeddings leverage well the grouping of finergrained categories into these blocks, which we call policy domain from now on. then, they are used to party text category afd the principles of equality before the law. equality: positive cdu we are explicitly committed to nato's 2% target. military: positive fdp and with a state that is strong because it acts lean and modern instead of complacent, old-fashioned and sluggish. ## conclusion in our first contribution, we introduce additive manifesto decomposition, a workflow for efficient analysis of party platforms, both in aggregate and across a range of policy issues. it builds on stateof-the-art sentence-representation models, which it uses for three operations on policy domains: definition, prediction, and (cross-party) similarity computation. in this manner, our workflow can incorporate advances on the representational level @xcite but complements them with a crucial level of reflection and analysis at the informative level of policy domains. our second contribution is a study of the political landscape in germany using our workflow. the results we obtain match well with expert judgements, suggesting that our workflow yields a reliable technique to automatically study the similarity between parties across policy domains. in addition to analysing the implicit stance space, operationalized through distance matrices derived from text similarity, we show that our method makes it possible to recover the traditional scaling analyses of the political science literature: we can efficiently approximate the aggregate rile (right-left) scores provided by experts in the aggregate settings, and when proceeding by domain, we see that our methods recover non-trivial policy configurations, e.g., the agreement of the far-right and far-left parties in germany on the subject of eu and the expansion of education. moreover, we show that classifiers substitute the annotations of these high-level domains and still yield similar results as compared to the fully annotated scenario. germany provided an appropriate target for our case study, given both the large number of annotated manifestos and large body of expert analyses. nevertheless, an important direction for future work is testing the applicability of our workflow to other countries, in particular regarding the training of policy domain labelers given the challenging concept drift between elections, and the possible cross-lingual application of our model components despite differences between political cultures @xcite . lastly, our methodology does not only suit the identification of the positioning in the political domain, but more broadly it can be seen as a different way of identifying the stance of an entity (person, organization, group). it can be applied whenever there is some aggregation of texts with regard to a set of entities. the distinction lies in the more fine-grained identification of stances: we (a) take larger chunks of text as input and (b) position the entities on a scale rather than characterizing them as in favor, neutral or against a given topic.