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Exploring Two Decades of Parliamentary Speeches on the Use of Narratives

Paper · 2026 · Matti Wiegmann, Jürgen Neyer and Benno Stein

Summary

Political scientists want to track how support for broad narratives changes over time. These narratives are seldom named explicitly, so keyword or metadata counts fail, and close reading cannot scale to decades of debate. The authors instead have an LLM judge, passage by passage, whether a text backs a given narrative, then count positive judgements per month to build a time series. They apply this to two rival views of European sovereignty: supranationalism (EU institutions should hold power) and nationalism (member states should). The data are 137,844 European Parliament speeches from 2006 onward, split into paragraphs and grouped by topic with BERTopic. On an expert-annotated test set, the prompt-based classifier reaches about 0.85 balanced accuracy with Qwen3-32B and roughly 0.75 with Qwen3-8B. The per-topic curves differ meaningfully over time and line up with real-world events. However, some topics are skewed toward one narrative, and sparsely discussed topics yield noisy signals. Developed jointly with political scientists, the pipeline is a practical starting point for diachronic discourse research.

Key points

  • Corpus: 137,844 EP speeches collected by Husmann and Wiegmann (2025), divided into 474,545 paragraphs. The paper gives the end year inconsistently as 2022 and 2023.
  • Test set: 400 keyword-sampled paragraphs (200 per narrative) were labelled by two political science graduate students. Krippendorff's α was 0.67–0.79, and the 367 unanimous cases were kept.
  • Prompt and models:
    • The prompt combines expert-written narrative definitions, filtering steps for off-topic passages, a chain-of-thought explanation, and four ICL examples.
    • Five models were compared: Llama-8B, Phi-4-4B, Qwen3-8B, Qwen3-32B and GPT-5.1-mini.
    • Balanced accuracy was the metric, since the test set is small and imbalanced.
  • Topic modelling: BERTopic with all-mpnet-base-v2, UMAP (10 components, 20 neighbours) and HDBSCAN (minimum cluster size 100).
  • Temporal aggregation:
    • The core signal is monthly positive counts per narrative and the share of supranational positives among all positives.
    • Only months with ≥10 positives are counted, and a B-spline smooths the ratio.
    • Density and prevalence scores help judge how reliable each topic's signal is.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Bauhaus-Universität WeimarWeimar · Germany10 works · 3,120 citations

Also on the byline: University of Kassel · European University Viadrina.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksSocial Sciences · 1,954 worksGeneral Social Sciences · 254 works

Cite

Cite as Matti Wiegmann, Jürgen Neyer and Benno Stein. 2026. Exploring Two Decades of Parliamentary Speeches on the Use of Narratives. Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4vdcyy75sgyv

@inproceedings{wiegmann2026exploring,
  title = {{Exploring Two Decades of Parliamentary Speeches on the Use of Narratives}},
  author = {Matti Wiegmann, Jürgen Neyer and Benno Stein},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)},
  year = {2026},
  doi = {10.63317/4vdcyy75sgyv},
  url = {https://doi.org/10.63317/4vdcyy75sgyv}
}

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