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LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade

Paper · 2025 · Aida Kostikova, Ole Putz, Steffen Eger et al.

Summary

The paper asks whether LLMs can reliably apply a demanding, theory-based coding scheme to political speech, and whether their labels support sound conclusions about trends over time. The authors use Thijssen's (2012) solidarity typology. It first sorts statements into solidarity, anti-solidarity, mixed or none, then assigns one of four rationales: group-based, exchange-based, compassionate or empathic. They compare many LLMs against human reference annotations, varying model size, prompting, fine-tuning and historical versus modern text. GPT-5 and gpt-oss-120B reach macro-F1 close to human agreement. However, their mistakes are systematic and shared across models, so raw outputs distort aggregate trends. To fix this, the authors feed soft-label outputs into Design-based Supervised Learning (DSL), which corrects estimates using a human-coded subset. They apply the corrected pipeline to German parliamentary debates from 1867 to 2025. The results show strong solidarity toward migrants after the war and a clear turn to anti-solidarity after 2015. The main lesson is that LLM annotation can work at scale only with validation and statistical correction.

Key points

  • The corpus covers the Reichstag (1867–1933), the West German Bundestag (1949–1990) and the re-unified Bundestag (1990–2025). Two periods get close study: 1949–1957 and 2009–2025.
  • GPT-5 scores highest. gpt-oss-120B is the strongest open model and nearly as good. Both reach macro-F1 comparable to human leave-one-out agreement.
  • Model errors are structured and correlated, so simple ensembles help little. When models err, they most often predict "None" rather than leaning toward anti-solidarity.
  • Raw model trends overstate group-based solidarity. DSL correction recovers the compassionate-dominant pattern that human annotations show.
  • The rise in anti-solidarity after 2015 does not come only from the AfD entering parliament. It also appears in CDU/CSU and FDP speeches. Model and human reliability both dip for some historical decades, such as the 1960s.

Interesting ideas

  • I1. Instead of counting hard model labels, the authors combine model probabilities with a small human-coded sample through DSL, which yields bias-corrected aggregate estimates; this decouples "good per-item accuracy" from "trustworthy totals".
  • I2. High scores from several strong models do not imply independent evidence, because their errors overlap, so ensembling or voting adds less than expected.
  • I3. The annotation scheme is two-tiered: a coarse stance decision comes first, and only then a finer label for the underlying rationale, which keeps the hard distinctions confined to a smaller set of cases.
  • I4. Human leave-one-out agreement serves as the realistic ceiling for model performance, which makes clear when a fine-grained category is inherently ambiguous.
  • I5. The authors break reliability down by time period and check which way errors lean (for example, toward "None"), rather than relying on a single global score.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Corpus-level statistics from silver data (e.g., frequencies of a construction or frame) could be corrected against a small gold sample instead of trusting raw silver counts.
  • I2 (learn): When building silver data from witness parsers, we should measure how correlated their errors are on gold before treating agreement as confirmation.
  • I3 (frames): Stance or speech-act annotation in prag could first fix polarity and only then the justification type, with each level reported separately in explanations.
  • I4 (learn): Evaluations of contested labels, such as semantic roles or irony, should report inter-annotator agreement alongside parser scores so that gaps are read correctly.
  • I5 (answer): Reader evaluations could be split by text era or genre (old fables vs. modern school texts), and checked for a default-to-"no answer" bias.

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

🎓Bielefeld UniversityBielefeld · Germany35 works · 1,196 citations🎓University of Technology NurembergNuremberg · Germany6 works · 30 citations

 Fields, topics and concepts

📚 Shelves 7The 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 worksSociology and Political Science · 626 worksGeneral Social Sciences · 254 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Aida Kostikova, Ole Putz, Steffen Eger, Olga Sabelfeld and Benjamin Paassen. 2025. LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade. arXiv:2509.07274.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2509.07274 · arXiv 2509.07274

@misc{kostikova2025llm,
  title = {{LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade}},
  author = {Aida Kostikova, Ole Putz, Steffen Eger, Olga Sabelfeld and Benjamin Paassen},
  year = {2025},
  doi = {10.48550/arxiv.2509.07274},
  eprint = {2509.07274},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2509.07274}
}

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