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The FIGNEWS Shared Task on News Media Narratives

Paper · 2024 · Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash et al.

Summary

FIGNEWS was a shared task at Arabic-NLP 2024, held alongside ACL 2024. Its goal was less about building classifiers and more about learning how to write annotation guidelines for subjective, politically charged judgments. The case study was multilingual news coverage of the opening months of the Israel War on Gaza. The organizers used CrowdTangle to gather public Facebook posts from verified accounts in English, French, Arabic, Hebrew, and Hindi. They kept the most-interacted posts around ten major events and split them into balanced batches, with Google Translate output into English and Arabic for annotators who did not read the source language. Participating teams wrote their own guidelines and labeled posts for bias, propaganda, or both. They were ranked on guideline design, annotation quality, volume, and consistency. Seventeen teams produced roughly 130K labels in total. A shared overlap subset made it possible to compare agreement across teams. The released data and code give a starting point for multilingual bias and propaganda research, and the paper argues for treating annotation as a science of its own.

Key points

  • Participation: 17 teams took part. Sixteen did the bias subtask and six did the propaganda subtask, together producing 129,800 data points across four tracks (guidelines, quality, quantity, consistency).
  • Data collection: Facebook posts containing "Gaza" were collected from October 7, 2023 to January 31, 2024, about 300,000 per language.
  • Sampling: The pool was reduced to 15,000 posts by taking the top 200 per language for each of ten key moments. These were split into 15 batches of 1,000, each balanced by language (200 per language) and by moment (20 per moment).
  • Agreement subset: In every batch, 10% of posts (20 per language, 1,500 in total) were set aside to measure inter-annotator agreement in the same way for all teams.
  • Label correlations: Pearson correlations of per-post team vote counts on Main Batches 1 and 2 (1,800 items) showed:
    • Bias Not Applicable vs. Propaganda Not Applicable: r=85.9%.
    • Unbiased vs. Not Propaganda: 78.7%.
    • Biased against Palestine vs. Propaganda: 74.3%.
    • Biased against Israel showed the reverse pattern, but much more weakly.
  • Release: The resources are published as CAMeL-Lab/FIGNEWS-2024 on GitHub and Hugging Face.

Interesting ideas

  • I1. Every batch contains the same small slice of shared items, so agreement between independent teams can be measured on identical material without re-annotating everything.
  • I2. The corpus is stratified by event and by language, so no single moment or language dominates any batch. This makes per-batch comparisons fair.
  • I3. The guidelines themselves were judged as a deliverable, next to label quality, volume, and consistency. This rewards a clear annotation scheme, not just a large output.
  • I4. Relations between two label schemes are discovered by correlating per-item vote counts across many annotators, with no aligned gold mapping needed.
  • I5. Machine translation was accepted as a deliberately noisy aid, on the grounds that it mirrors how people actually read foreign news.

For Mova

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

  • I1 (learn): Insert a fixed common subset into each silver-data batch, so agreement among witness parsers and LLM teacher tiers is always measured on the same items.
  • I2 (answer): Build test sets for fables, word problems, and school tests stratified by text type and difficulty, so scores from different runs stay comparable.
  • I3 (learn): Score the instructions given to LLM teachers (prompt and guideline versions) by the agreement and quality they produce, and keep only the best-performing ones.
  • I4 (frames): Correlate annotator or witness votes on speech-act, irony, and implicature labels to find which pragmatic categories co-occur or overlap.
  • I5 (back): Compare frames extracted from an original text with frames extracted from its machine translation, as a check that translation through the meta-world preserves who-did-what-to-whom.

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

🏢GoogleMountain View · United States430 works · 424,363 citations🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations🎓Carnegie Mellon University QatarDoha · Qatar47 works · 2,773 citations🎓Birzeit UniversityBirzeit · Palestine36 works · 798 citations🎓Northwestern University in QatarDoha · Qatar24 works · 148 citations🎓New Giza UniversityGiza · Egypt3 works · 54 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash, Houda Bouamor, Imed Zitouni, Mona Diab, Samhaa El-Beltagy and Muhammed AbuOdeh. 2024. The FIGNEWS Shared Task on News Media Narratives. Proceedings of the Second Arabic Natural Language Processing Conference.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.arabicnlp-1.56

@inproceedings{zaghouani2024fignews,
  title = {{The FIGNEWS Shared Task on News Media Narratives}},
  author = {Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash, Houda Bouamor, Imed Zitouni, Mona Diab, Samhaa El-Beltagy and Muhammed AbuOdeh},
  booktitle = {Proceedings of the Second Arabic Natural Language Processing Conference},
  year = {2024},
  doi = {10.18653/v1/2024.arabicnlp-1.56},
  url = {https://doi.org/10.18653/v1/2024.arabicnlp-1.56}
}

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