Complementing Kernel Density Estimation and Topic Modelling to Visualise Political Discourse

We examine how politicians shape and convey policy issues among different communication channels and how well we can visualise issue ownership and issue framing using data-driven methods. Drawing from a political communication approach, we propose to complement two methods – Kernel density estimation and topic modelling – to visualise political discourse. Our case study is established on two main data sources: transcripts of parliamentary debates and tweets of Swiss elected politicians. We propose a two-step methodology. First, we use topic modelling to identify the main policy issues emphasised by politicians. Second, we use the topical content and political affiliation as meta-information in Kernel density estimation to calculate and display the distances between important features, the main extracted topic for each document, and political affiliation. We compare the obtained results from the transcripts of parliamentary debates and from Twitter data. Using conceptual visualisation maps enables us to qualitatively discuss whether the findings are indicative of issue ownership and stylistic markers pointing to issue framing. The proposed methodology can be applied to a variety of research questions in the realm of communication studies with an interdisciplinary and exploratory focus. It also provides a suitable way to account for polarisation processes that can be linked to affordances of communication channels and to institutional specificities of the national political context