---
title: "How Loud Rumbles Hit Newsstands: A Data Analysis of"
description: "The paper asks how German newspapers cover landslides outside Germany, and whether that coverage… 0.865 recall. 46 references."
authors: ["Brielen Madureira", "Andreas Niekler", "Marc Keuschnigg", "Mariana Madruga de Brito"]
year: 2026
venue: "arXiv:2605.18105"
source: "https://arxiv.org/abs/2605.18105"
doi: "10.48550/arxiv.2605.18105"
arxiv: "2605.18105"
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/madureira-2026-loud-rumbles-hit-newsstands-data/"
---
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# How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World

**Authors:** [Brielen Madureira](https://md.research.elamur.ai/free/en/authors/brielen-madureira/), [Andreas Niekler](https://md.research.elamur.ai/free/en/authors/andreas-niekler/), Marc Keuschnigg and [Mariana Madruga de Brito](https://md.research.elamur.ai/free/en/authors/mariana-madruga-de-brito/) · **Year:** 2026 · **Venue:** arXiv:2605.18105 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2605.18105) · [PDF](https://arxiv.org/pdf/2605.18105) · [DOI 10.48550/arxiv.2605.18105](https://doi.org/10.48550/arxiv.2605.18105) · [arXiv 2605.18105](https://arxiv.org/abs/2605.18105) · [OpenAlex](https://openalex.org/W7161691546)

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

## Summary
The paper asks how German newspapers cover landslides outside Germany, and whether that coverage matches how often such disasters happen. The authors start with a wiso-net keyword retrieval of German articles from 2000–2024. They filter it with a locally run open-weight LLM (google/gemma-4-31b) in two stages: first deciding whether a text reports real landslides, then extracting the affected countries as ISO-3 codes. Both stages are checked against a human-annotated sample.

Each country's daily article counts are then split into "news events" with a simple gap rule. The event counts are compared with two external references: the EM-DAT disaster inventory and the World Bank's Global Landslide Hazard Map. The final dataset has roughly 55k articles grouped into about 4.5k news events. Coverage turns out to be uneven: Southern and Western Europe get more attention than their landslide occurrence would predict, while other regions are overlooked.

This matters in two ways. News-derived data can fill gaps in incomplete global disaster databases. It also gives a measurable basis for studying inequality in media attention to international disasters.

## Key points
- The pipeline starts from 317,036 retrieved articles (17 German landslide keywords). Preprocessing leaves 88,940 candidates, and 450 of them were human-annotated (58.6% relevant).
- On the gold sample, relevance classification reached precision 0.865, recall 0.973 and F1 0.916. Country geolocation reached 0.875 for exact matches and 0.924 when any country overlapped.
- A news event is a run of active days in one country's daily count series. Five or more consecutive days without articles start a new event, so gaps of up to four days are tolerated within one event.
- The external comparison covers 244 countries. Indicators include EM-DAT landslide counts (2,071 events for 2000–2024), WB-GLHM predicted annual frequency, ThinkHazard! risk class, UNCTAD development status and World Bank income group.
- The texts cannot be shared and must be accessed through wiso-net. The code is published on Codeberg (briemadu/landslides-german-news).

## Interesting ideas
- The LLM is not asked for everything at once: it first gives a cheap yes/no relevance decision, and only accepted texts go to a second prompt that returns strict JSON. This keeps the extraction step focused and each stage can be scored separately.
- The authors read all 40 false positives by hand and found that most were annotation mistakes or texts about landslide risk, not real model errors. Auditing disagreements before trusting a metric shows whether the gold standard or the model is at fault.
- To show that a lenient "any overlap" accuracy was not inflated by long lists of guessed countries, they compared how many countries per document the model output versus the gold labels. This acts as a sanity control on the metric itself.
- Raw mentions are grouped into episodes by a single gap threshold, and the threshold is stated openly as a hyperparameter with clear trade-offs (too low splits one story, too high merges separate ones).
- The results are validated against two references of different kinds: an inventory of observed, impact-filtered events and a predictive environmental model. Disagreements between them, such as Canada, are explained from how each source was built.

## For Mova

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

- **I1** (learn): LLM teacher tiers could be run the same way, with a gate question first and a structured silver label only for items that pass, each tier with its own score on gold.
- **I2** (learn): when witness parsers disagree with a treebank, manually triaging a sample would separate parser faults from gold errors before rules or training data are changed.
- **I3** (answer): QA scoring that accepts partial matches could include a check that the answer-set sizes match gold, so over-listing cannot game the score.
- **I4** (world): mentions of the same happening scattered through a long text could be clustered into one event by a transparent distance threshold, keeping event ordering explainable.
- **I5** (world): facts compiled from Wikidata and WordNet/FrameNet could be cross-checked against each other, with conflicts traced to each source's inclusion criteria rather than silently resolved.

*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

- [Leipzig University](https://md.research.elamur.ai/free/en/universities/leipzig-university/) — University, Leipzig, Germany · 26 works · 8,622 citations
- [Linköping University](https://md.research.elamur.ai/free/en/universities/linkoping-university/) — University, Linköping, Sweden · 18 works · 2,319 citations
- [Helmholtz Centre for Environmental Research](https://md.research.elamur.ai/free/en/labs/helmholtz-centre-for-environmental-research/) — Research institute, Leipzig, Germany · 3 works · 1 citation

📚 Shelves (4): [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Communication](https://md.research.elamur.ai/free/en/shelves/social-sciences/communication/) · 97 works · [🌿 Environmental Science](https://md.research.elamur.ai/free/en/shelves/environmental-science/) · 9 works · [🌿 Management, Monitoring, Policy and Law](https://md.research.elamur.ai/free/en/shelves/environmental-science/management-monitoring-policy-and-law/) · 2 works

## Cite

Cite as: Brielen Madureira, Andreas Niekler, Marc Keuschnigg and Mariana Madruga de Brito. 2026. How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World. arXiv:2605.18105.

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

```bibtex
@misc{madureira2026how,
  title = {{How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World}},
  author = {Brielen Madureira, Andreas Niekler, Marc Keuschnigg and Mariana Madruga {de Brito}},
  year = {2026},
  doi = {10.48550/arxiv.2605.18105},
  eprint = {2605.18105},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2605.18105}
}
```

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## References

46 references, 1 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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*Updated 1 November 2026*