Inferring political spaces from data
Spatial metaphors are ubiquitous in political communication. One talks about political positions, political landscapes, and about the distances between opinions or positions of political actors. Thus, it is natural to consider spatial representations of political opinions: Political spaces. The main level of political discourse, that can be observed directly, is the level of political issues. We consider anything on which a political decision can be made as a political issue. The issue space is spanned by the possible attitudes on issues that are the object of political decisions in all fields of politics. This space itself has a multi-level structure, because these issues can be considered at different levels of aggregation. For a single actor, such as a politician, a political party, or a voter the attitude towards these issues is represented as a point (or region) in this issue space. If a population of such actors is considered, the attitudes on the single issues will be normally not statistically independent. Due to these correlations, the attitudes of the population might be approximately represented in a low dimensional space, called “political spaces”. In the present contribution I will discuss, how such political spaces can be inferred from textual data on two examples: Electoral manifestos from the Manifesto project (https://manifesto-project.wzb.eu/) and data from Twitter.