---
title: "SemEval-2023 Task 3 — paper by Piskorski et al. (2023)"
description: "This paper presents a SemEval-2023 shared task on news analysis across languages. 62 references, cited by 64 papers here."
authors: ["Jakub Piskorski", "Nicolas Stefanovitch", "Giovanni Da San Martino", "Preslav Nakov"]
year: 2023
venue: "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)"
source: "https://aclanthology.org/2023.semeval-1.317/"
doi: "10.18653/v1/2023.semeval-1.317"
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/piskorski-2023-semeval-2023-task-3-detecting/"
---
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# SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup

**Authors:** [Jakub Piskorski](https://md.research.elamur.ai/free/en/authors/jakub-piskorski/), Nicolas Stefanovitch, [Giovanni Da San Martino](https://md.research.elamur.ai/free/en/authors/giovanni-da-san-martino/) and [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/) · **Year:** 2023 · **Venue:** [Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2023](https://md.research.elamur.ai/free/en/venues/semeval/2023/)) · **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/2023.semeval-1.317/) · [PDF](https://aclanthology.org/2023.semeval-1.317.pdf) · [DOI 10.18653/v1/2023.semeval-1.317](https://doi.org/10.18653/v1/2023.semeval-1.317) · [OpenAlex](https://openalex.org/W4385572111)

**Citations:** 86 citations (OpenAlex, 2026-10-14) · [64](https://md.research.elamur.ai/free/en/papers/piskorski-2023-semeval-2023-task-3-detecting/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
This paper presents a SemEval-2023 shared task on news analysis across languages. It goes beyond earlier propaganda-detection challenges by combining three views of an article. The first is its genre: opinion, objective reporting, or satire. The second is the framing, a multi-label choice among the 14 generic dimensions of the Media Frames Corpus. The third is the persuasion techniques present in each paragraph, labelled with an expanded taxonomy of 23 techniques grouped into six broader categories. The organizers built an annotated multilingual dataset of news on contested topics such as COVID-19, migration, climate change and the Russo-Ukrainian war. Participants knew six training languages in advance. Spanish, Greek and Georgian appeared only at test time, which tests cross-lingual transfer. Participation was large: 181 teams registered and 41 submitted official test runs. Across all subtasks, the strongest systems beat the organizers' baselines, but paragraph-level technique detection stayed hard, with top average micro F1 below 0.45. The resource matters because it supports comparative, cross-country study of how media frame and push narratives.

## Key points
- **Languages:** nine in total.
  - Known in advance: English, French, German, Italian, Polish and Russian.
  - Surprise test-only languages: Spanish, Greek and Georgian.
- **Subtasks:**
  - ST1 is multi-class genre classification at the article level.
  - ST2 is multi-label frame detection at the article level, using the 14 frames from Card et al. (2015).
  - ST3 is multi-label persuasion technique detection at the paragraph level, with 23 techniques in a two-level hierarchy.
- **Technique taxonomy:** the six coarse groups are Attack on reputation, Justification, Simplification, Distraction, Call, and Manipulative wording.
- **Participation:** 181 registrations, 41 official submissions and 32 system description papers.
- **Best averages on the six main languages:**
  - ST1 macro score: SheffieldVeraAI reached 0.779, against a baseline of 0.711.
  - ST2 micro score: MarsEclipse reached 0.594, against a baseline of 0.412.
  - ST3 micro score: KInITVeraAI reached 0.446, against a baseline of 0.256.

## Interesting ideas
- **Surprise languages:** withholding some languages until the test phase gives a clean measure of zero-shot cross-lingual transfer. This setup would suit evaluating Ukrainian or other low-resource NLP models.
- **Two-tier label hierarchy:** organizing fine labels under coarse categories allows analysis at either granularity. It also gives sparse, rare classes a fallback level, which helps for any large and imbalanced label set.
- **Paragraph-level labels:** annotating techniques per paragraph rather than per exact span makes annotation and evaluation simpler. Detection stays more localized than article-level labels would allow.
- **Results by language group:** scores are reported separately for the provided languages, the surprise languages, and all nine. Reporting micro and macro F1 side by side also shows the effect of class imbalance. This helps separate in-distribution skill from generalization.
- **Annotation quality control:** the organizers kept a detailed guideline document (about 60 pages), revised it as new tricky cases appeared, and removed annotators who performed poorly. This is a practical recipe for subjective annotation tasks.

*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

- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 47 works · 4,456 citations
- [Polish Academy of Sciences](https://md.research.elamur.ai/free/en/government/polish-academy-of-sciences/) — Government body, Warsaw, Poland · 8 works · 512 citations
- [Institute of Computer Science](https://md.research.elamur.ai/free/en/labs/institute-of-computer-science/) — Research institute, Warsaw, Poland · 6 works · 468 citations
- [European Commission](https://md.research.elamur.ai/free/en/government/european-commission/) — Government body, Brussels, Belgium · 3 works · 963 citations
- [Joint Research Centre](https://md.research.elamur.ai/free/en/government/joint-research-centre/) — Government body, Ispra, Italy · 3 works · 963 citations

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## Cite

Cite as: Jakub Piskorski, Nicolas Stefanovitch, Giovanni Da San Martino and Preslav Nakov. 2023. SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup. Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023).

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

```bibtex
@inproceedings{piskorski2023semeval,
  title = {{SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup}},
  author = {Jakub Piskorski, Nicolas Stefanovitch, Giovanni {Da San Martino} and Preslav Nakov},
  booktitle = {Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  year = {2023},
  doi = {10.18653/v1/2023.semeval-1.317},
  url = {https://doi.org/10.18653/v1/2023.semeval-1.317}
}
```

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## References

62 references, 7 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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60. Ben Wu, Olesya Razuvayevskaya, Freddy Heppell, João Leite, Carolina Scarton, Kalina Bontcheva, and Xingyi Song. 2023. Sheffieldveraai at semeval-2023 task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification. In Proceedings of the 17th International Works… [find](https://scholar.google.com/scholar?q=Ben+Wu%2C+Olesya+Razuvayevskaya%2C+Freddy+Heppell%2C+Jo%C3%A3o+Leite%2C+Carolina+Scarton%2C+Kalina+Bontcheva%2C+and+Xingyi+Song%2E+2023%2E+Sheffieldveraai+at+semeval%2D2023+task+3%3A+Mono+and+multilingual+approaches+for+news+)
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62. Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, Kareem Darwish, James Glass, and Preslav Nakov. 2019. Tanbih: Get to know what you are reading. In Proceedings of the Conference on Empi… [find](https://scholar.google.com/scholar?q=Yifan+Zhang%2C+Giovanni+Da+San+Martino%2C+Alberto+Barr%C3%B3n%2DCede%C3%B1o%2C+Salvatore+Romeo%2C+Jisun+An%2C+Haewoon+Kwak%2C+Todor+Staykovski%2C+Israa+Jaradat%2C+Georgi+Karadzhov%2C+Ramy+Baly%2C+Kareem+Darwish%2C+James+Glass%2C+and+Pre)

*Updated 24 October 2026*