---
title: "Ideology Prediction of German Political Texts — Schneider 2026"
description: "The paper looks at how to place German political text on a continuous left–right axis, d ∈ [−1, 1]. 50 references."
authors: ["Sinclair Schneider", "Florian Steuber", "Joao A. G. Schneider", "Gabi Dreo Rodosek"]
year: 2026
venue: "arXiv:2605.14352"
source: "https://arxiv.org/abs/2605.14352"
doi: "10.48550/arxiv.2605.14352"
arxiv: "2605.14352"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schneider-2026-ideology-prediction-german-political-texts/"
---
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# Ideology Prediction of German Political Texts

**Authors:** Sinclair Schneider, Florian Steuber, Joao A. G. Schneider and Gabi Dreo Rodosek · **Year:** 2026 · **Venue:** arXiv:2605.14352 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2605.14352) · [PDF](https://arxiv.org/pdf/2605.14352) · [DOI 10.48550/arxiv.2605.14352](https://doi.org/10.48550/arxiv.2605.14352) · [arXiv 2605.14352](https://arxiv.org/abs/2605.14352) · [OpenAlex](https://openalex.org/W7161258553)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper looks at how to place German political text on a continuous left–right axis, d ∈ [−1, 1]. Earlier work mostly sorts text into fixed classes such as left, centre and right. A continuous score lets analysts pick out any band of the spectrum. The authors built four corpora:
- Bundestag plenary speeches, auto-labelled from recorded interjections and applause
- statements from the Wahl-O-Mat voting-advice tool
- articles from 33 German newspapers with known leanings
- 535,200 tweets from 597 members of parliament

The first two corpora were used for training and the last two only for out-of-domain testing. They compared 13 transformers (BERT-family, Llama and Gemma). Each model predicts which parties would agree with a text. Every party is assigned a unit vector at its angle on the spectrum, and the angle of the weighted vector sum becomes the score. DeBERTa-large performed best in-domain and on tweets, while Gemma2-2B was best on newspapers. The authors conclude that architecture and domain-matched data can matter as much as model size for this task.

## Key points
- The Bundestag protocols cover October 2017 to September 2024 (34,174 speeches) and yield 32,246 party pro/contra statements. Labels come from interruptions such as applause or heckling, typed by party; speeches with no interruptions were dropped.
- Llama 3.1 rewrote each text in five registers to add linguistic variety: child, teenager, adult, eloquent speaker, and tweet.
- In-domain, DeBERTa-large reached F1 = 0.844.
- On tweets, DeBERTa-large predicted the author's party with ACC = 0.864 once 100+ words were available.
- On the 33 newspapers, Gemma2-2B reached MAE = 0.172, about 8.58% error against a survey-based benchmark of outlet leanings.
- Stated limitations:
  - The scale is bounded by the outermost parties (Die Linke, AfD), so more extreme positions cannot be told apart from them.
  - AfD and Die Linke sometimes agree, possibly because both are in opposition.
  - The classifier offers no explanation of its decisions.
  - It needs retraining as political positions drift.

## Interesting ideas
- Parliament transcripts record who applauded or heckled whom, which gives free stance labels without manual annotation; structural side-signals in a document can serve as a cheap weak supervisor.
- Asking an LLM to restate each training text in several registers (childlike, eloquent, tweet-like) widens stylistic coverage, so a model trained on formal speech transfers better to informal text.
- A continuous score is obtained from a categorical model by giving each class a unit vector at a meaningful angle and reading off the angle of the probability-weighted sum.
- The authors train on two sources and evaluate only on two unrelated sources, so the test measures transfer instead of memorised in-domain cues.
- Results are reported as a function of how much text is available, and accuracy rises once 100+ words are present.

## For Mova

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

- **I1** (learn): We could mine similar by-product signals (dialogue replies, quoted reactions, markup) as extra silver evidence next to the witness parsers, scoring them against negative controls.
- **I2** (tree): Register-shifted rewrites of treebank-style sentences, parsed and checked for agreement, could harden gparse and the perceptron parser against casual or social-media English.
- **I3** (world): The same trick could turn verb-class or sentiment distributions into graded values on a scale (e.g., degree of approval), while keeping the per-class contributions visible for explanation.
- **I4** (learn): Holding out entire genres, such as fables or maths problems, as test-only material would give a more honest read on whether induced repair rules generalise.
- **I5** (answer): Breaking down reader accuracy by passage length would show where answers become unreliable and when Mova should decline or hedge.

*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

Also on the byline: [Universität der Bundeswehr München](https://ror.org/05kkv3f82) · [Bundeswehr](https://ror.org/00nmgny79).

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## Cite

Cite as: Sinclair Schneider, Florian Steuber, Joao A. G. Schneider and Gabi Dreo Rodosek. 2026. Ideology Prediction of German Political Texts. arXiv:2605.14352.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{schneider2026ideology,
  title = {{Ideology Prediction of German Political Texts}},
  author = {Sinclair Schneider, Florian Steuber, Joao A. G. Schneider and Gabi Dreo Rodosek},
  year = {2026},
  doi = {10.48550/arxiv.2605.14352},
  eprint = {2605.14352},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2605.14352}
}
```

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## References

50 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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