Publication: Complementing Kernel Density Estimation and Topic Modelling to Visualise Political Discourse
Complementing Kernel Density Estimation and Topic Modelling to Visualise Political Discourse
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Reveilhac, M., & Schneider, G. (2022). Complementing Kernel Density Estimation and Topic Modelling to Visualise Political Discourse (J. H. Jantunen & et al, Eds.; pp. 12–27). University of Jyväskylä. https://jyx.jyu.fi/handle/123456789/84140
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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
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Publications of Department of Communication and Media Research
Publications of Digital Society Initiative
Publications of Linguistic Research Infrastructure (LiRI)
Publications of URPP Digital Religion(s)
Publications of Zurich Center for Linguistics
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Reveilhac, M., & Schneider, G. (2022). Complementing Kernel Density Estimation and Topic Modelling to Visualise Political Discourse (J. H. Jantunen & et al, Eds.; pp. 12–27). University of Jyväskylä. https://jyx.jyu.fi/handle/123456789/84140
