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In:Mathematical Modelling in Linguistics and Text Analysis: Theory and applications
Edited by Adam Pawłowski, Sheila Embleton, Jan Mačutek and Aris Xanthos
[Current Issues in Linguistic Theory 370] 2025
► pp. 191–206

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Measuring language complexity about European politics in Swiss parliamentary debates

ORCiD logo with link | University of Zurich
ORCiD logo with link | University of Zurich
Published online: 13 October 2025
DOI logo with link
https://doi.org/10.1075/cilt.370.16rev
Abstract
This study investigates changes in the political discourse surrounding Europe and European integration in Swiss parliamentary debates from 1995 to 2022. It relies on text analysis methods for measuring linguistic complexity and benchmarks the observed trends against topicality, party affiliation and other information sources. Analysis of variance assesses how linguistic complexity indicators vary across these factors, principal component analysis identifies the multidimensionality of complexity in parliamentary speeches, and an experiment is conducted with ChatGPT to reduce the complexity of the most complex parliamentary speeches about Europe. Results highlight a decline in complexity over time in speeches related to the EU debate and overall. Furthermore, the study identifies four key dimensions of complexity (technicality, clarity, accessibility and familiarity), and shows the intervention of ChatGPT to reduce technicality and improve familiarity of complex political speeches.
Article outline
  • 1.Introduction
  • 2.Theoretical background
    • 2.1Measurement of complexity
      • 2.1.1Exploring language complexity in sociolinguistics and political discourse
      • 2.1.2Assessing complexity in political discourse
    • 2.2Strategic incentives of complexity
  • 3.Data and measures
    • 3.1Data sources
    • 3.2Measures
    • 3.3Analytical strategy
  • 4.Results
    • 4.1Benchmarking trends in linguistic complexity indicators
    • 4.2Extracting relevant political text complexity dimensions
    • 4.3Experiment: Potential of ChatGPT to reduce complex speeches
  • 5.Discussion of the main findings
  • Note
  • References
  • Appendix
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