---
title: "DiNO: Disinformation Narrative Observer (2026)"
description: "The authors propose DiNO, a four-stage pipeline: Split articles into candidate spans with a new segmentation scheme… 102 references."
authors: ["Witold Sosnowski", "Arkadiusz Modzelewski", "Kinga Skorupska", "Adam Wierzbicki"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.2160/"
doi: "10.18653/v1/2026.acl-long.2160"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sosnowski-2026-dino-disinformation-narrative-observer/"
---
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# DiNO: Disinformation Narrative Observer

**Authors:** [Witold Sosnowski](https://md.research.elamur.ai/free/en/authors/witold-sosnowski/), [Arkadiusz Modzelewski](https://md.research.elamur.ai/free/en/authors/arkadiusz-modzelewski/), Kinga Skorupska and [Adam Wierzbicki](https://md.research.elamur.ai/free/en/authors/adam-wierzbicki/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.2160/) · [PDF](https://aclanthology.org/2026.acl-long.2160.pdf) · [DOI 10.18653/v1/2026.acl-long.2160](https://doi.org/10.18653/v1/2026.acl-long.2160) · [OpenAlex](https://openalex.org/W7166801819)

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

## Summary

The paper asks how to recover the disinformation narratives that a specific news outlet spreads. Narratives here are broad recurring messages built from many related false claims. The authors propose DiNO, a four-stage pipeline:

1. Split articles into candidate spans with a new segmentation scheme, DiNO-sgm, and match those spans to fact-checked false claims by semantic similarity.
2. Use an LLM to check that the article actually endorses each matched claim rather than refuting it.
3. Embed the confirmed segments, reduce their dimensionality and cluster them.
4. Have an LLM condense each cluster into a short, neutral narrative statement.

DiNO was applied to coverage of the Ukraine War, COVID-19 and Migration from disinformation-prone outlets and from The Guardian. The extracted narratives were scored against a reference collection of known disinformation narratives. DiNO clearly beat the narrative-mining baselines Relatio and CaNarEx on both topic and stance agreement. It also found few narratives in the reputable outlet. This makes it a practical, auditable tool for monitoring disinformation at the outlet level.

## Key points

- **Data:** twelve datasets in total.
  - Nine contain about 130k news articles from Sputnik, Pravda, NaturalNews, Breitbart and The Guardian.
  - Three contain about 26k verified false claims, drawn from sources such as EUvsDisinfo and the Google Fact Check Tool API.
- **Main results:** DiNO scored 0.75 on topical alignment, versus 0.52–0.53 for the baselines. On stance alignment it scored 0.85, versus 0.60–0.65. These correspond to gains of 41–44% and 30–41%.
- **Credible outlet:** The Guardian produced only about two narratives per domain, and these had opposed or contextual stances.
- **Cost:** a full run in the best configuration costs roughly $100 USD.
- **Release:** code, prompts, annotations, extracted narratives and dataset materials are publicly available (GitHub wsosnowski/DiNO).

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

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## Cite

Cite as: Witold Sosnowski, Arkadiusz Modzelewski, Kinga Skorupska and Adam Wierzbicki. 2026. DiNO: Disinformation Narrative Observer. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{sosnowski2026dino,
  title = {{DiNO: Disinformation Narrative Observer}},
  author = {Witold Sosnowski, Arkadiusz Modzelewski, Kinga Skorupska and Adam Wierzbicki},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2160},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2160}
}
```

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51. Deborah Miori and Constantin Petrov. 2025. Narratives from gpt-derived networks of news and a link to financial markets dislocations. International Journal of Data Science and Analytics, 20(2):1105–1129. [find](https://scholar.google.com/scholar?q=Deborah+Miori+and+Constantin+Petrov%2E+2025%2E+Narratives+from+gpt%2Dderived+networks+of+news+and+a+link+to+financial+markets+dislocations%2E+International+Journal+of+Data+Science+and+Analytics%2C+20%282%29%3A1105%E2%80%9311)
52. Arkadiusz Modzelewski, Giovanni Da San Martino, Pavel Savov, Magdalena Anna Wilczyńska, and Adam Wierzbicki. 2024. Mipd: Exploring manipulation and intention in a novel corpus of polish disinformation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages… [find](https://scholar.google.com/scholar?q=Arkadiusz+Modzelewski%2C+Giovanni+Da+San+Martino%2C+Pavel+Savov%2C+Magdalena+Anna+Wilczyn%CC%81ska%2C+and+Adam+Wierzbicki%2E+2024%2E+Mipd%3A+Exploring+manipulation+and+intention+in+a+novel+corpus+of+polish+disinformatio)
53. Arkadiusz Modzelewski, Witold Sosnowski, Eleni Pa-padopulos, Elisa Sartori, Tiziano Labruna, Giovanni Da San Martino, and Adam Wierzbicki. 2026. [Malicious intent dataset and inoculating llms for enhanced disinformation detection](https://md.research.elamur.ai/free/en/papers/modzelewski-2026-malicious-intent-dataset-inoculating-llms/). In Proceedings of the 19th Conference of the European Chapter of the A…
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58. Nikolaos Nikolaidis, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, et al. 2025. Polynarrative: A multilingual, multilabel, multi-domain dataset for narrative extractio… [find](https://scholar.google.com/scholar?q=Nikolaos+Nikolaidis%2C+Nicolas+Stefanovitch%2C+Purifica%C3%A7%C3%A3o+Silvano%2C+Dimitar+Iliyanov+Dimitrov%2C+Roman+Yangarber%2C+Nuno+Guimar%C3%A3es%2C+Elisa+Sartori%2C+Ion+Androutsopoulos%2C+Preslav+Nakov%2C+Giovanni+Da+San+Martino%2C+)
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75. Matúš Pikuliak, Ivan Srba, Robert Moro, Timo Hro-madka, Timotej Smoleň, Martin Melišek, Ivan Vykopal, Jakub Simko, Juraj Podroužek, and Maria Bielikova. 2023. Multilingual previously fact-checked claim retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Process… [find](https://scholar.google.com/scholar?q=Mat%C3%BA%C5%A1+Pikuliak%2C+Ivan+Srba%2C+Robert+Moro%2C+Timo+Hro%2Dmadka%2C+Timotej+Smolen%CC%8C%2C+Martin+Meli%C5%A1ek%2C+Ivan+Vykopal%2C+Jakub+Simko%2C+Juraj+Podrou%C5%BEek%2C+and+Maria+Bielikova%2E+2023%2E+Multilingual+previously+fact%2Dchecked+cla)
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78. Shaden Shaar, Nikolay Babulkov, Giovanni Da San Martino, and Preslav Nakov. 2020. [That is a known lie: Detecting previously fact-checked claims](https://md.research.elamur.ai/free/en/papers/shaar-2020-that-is-known-lie-detecting/). In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 3607–3618.
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86. Sputnik. 2025c. Sputnikglobe search results for ukraine. [find](https://scholar.google.com/scholar?q=Sputnik%2E+2025c%2E+Sputnikglobe+search+results+for+ukraine%2E)
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*Updated 1 November 2026*