---
title: "Automated stance detection in complex topics and small"
description: "The paper asks whether automatic stance detection works in a hard setting: Estonian, a… 46 references, cited by 8 papers here."
authors: ["Mark Mets", "Andres Karjus", "Indrek Ibrus", "Maximilian Schich"]
year: 2023
venue: "arXiv:2305.13047 · Plos one, 19(4), e0302380 (2024)"
source: "https://arxiv.org/abs/2305.13047"
doi: "10.1371/journal.pone.0302380"
arxiv: "2305.13047"
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/mets-2023-automated-stance-detection-complex-topics/"
---
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# Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media

**Authors:** Mark Mets, [Andres Karjus](https://md.research.elamur.ai/free/en/authors/andres-karjus/), Indrek Ibrus and Maximilian Schich · **Year:** 2023 · **Venue:** [arXiv:2305.13047 · Plos one, 19(4), e0302380 (2024)](https://md.research.elamur.ai/free/en/venues/plos-one/) ([PLoS ONE 2023](https://md.research.elamur.ai/free/en/venues/plos-one/2023/)) · **Publisher:** [Public Library of Science](https://md.research.elamur.ai/free/en/publishers/public-library-of-science/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1371/journal.pone.0302380) · [PDF](https://arxiv.org/pdf/2305.13047) · [DOI 10.1371/journal.pone.0302380](https://doi.org/10.1371/journal.pone.0302380) · [arXiv 2305.13047](https://arxiv.org/abs/2305.13047) · [OpenAlex](https://openalex.org/W4395661831)

**Citations:** 35 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/mets-2023-automated-stance-detection-complex-topics/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether automatic stance detection works in a hard setting: Estonian, a morphologically rich language with relatively few NLP resources, and immigration, a politically charged topic. The authors selected about 8000 topic-relevant sentences from two news sources, mainstream Ekspress Grupp and the right-wing populist portal Uued Uudised, published 2015–2022, using a keyword and stem lexicon. Human annotators labeled the sentences as pro- or anti-immigration. Several pretrained multilingual and Estonian-specific LLMs were fine-tuned on these labels. The authors compared them with ChatGPT 3.5 used as a zero-shot classifier and with a classifier tuned on general sentiment data.

Fine-tuned models reached acceptable accuracy, and ChatGPT came close to the best of them. The sentiment-based shortcut did poorly, because sentences with a negative tone can still support immigration. Applied to the full corpus, the best model traced stance shifts over seven years that line up with real-world events.

This matters because it suggests instructable LLMs could make media monitoring cheaper in small languages, provided they are validated against annotated data.

## Key points
- The released dataset contains 7345 stance-annotated Estonian sentences. Data and code are at github.com/markmets/immigration-prediction-EST.
- ChatGPT 3.5 as a zero-shot classifier performed nearly as well as the top fine-tuned LLM. Bootstrapping stance from a generic sentiment classifier did not work well.
- The diachronic analysis contrasts Uued Uudised with Ekspress Grupp outlets. Stance changes are linked to:
  - the 2015–2016 migrant crisis,
  - the 2018–2019 UN migration pact debate and the 2019 Estonian elections,
  - the 2022 Russian invasion of Ukraine.
- In a qualitative error analysis, both annotators and models struggled with sarcasm, dog-whistles and context-dependent opinions. Choosing sentences versus paragraphs as the unit of analysis is a trade-off between too little context and mixed stances.
- Reproducibility is a stated concern for prompt-based methods: results can shift with wording, with model randomness, and with updates to cloud-hosted models.

*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

- [Estonian Business School](https://md.research.elamur.ai/free/en/universities/estonian-business-school/) — University, Tallinn, Estonia · 2 works · 59 citations
- [Tallinn University](https://md.research.elamur.ai/free/en/universities/tallinn-university/) — University, Tallinn, Estonia · 2 works · 59 citations

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## Cite

Cite as: Mark Mets, Andres Karjus, Indrek Ibrus and Maximilian Schich. 2023. Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media. arXiv:2305.13047 · Plos one, 19(4), e0302380 (2024).

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

```bibtex
@misc{mets2023automated,
  title = {{Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media}},
  author = {Mark Mets, Andres Karjus, Indrek Ibrus and Maximilian Schich},
  year = {2023},
  doi = {10.1371/journal.pone.0302380},
  eprint = {2305.13047},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1371/journal.pone.0302380}
}
```

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*Updated 30 October 2026*