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Nakba Discourse 2025: A Bilingual Social Media Dataset for Collective Trauma Analysis

Paper · 2026 · Wajdi Zaghouani, Mabrouka Bessghaier and Kais Attia

Summary

Existing computational resources on Israel–Palestine online discourse mostly cover short escalation episodes or a single language, and many lack engagement data. As a result, sustained memory work around the 1948 Nakba is understudied. The authors address this with Nakba Discourse 2025, a corpus of Arabic and English posts collected throughout 2025 using Nakba-related keyword queries. Twitter/X posts came via Meltwater and Facebook posts via CrowdTangle. The raw harvest was cleaned in two steps: language identification with langdetect and fastText, then MinHash LSH near-duplicate removal. The cleaned corpus is split into twelve overlapping sub-corpora by sentiment, language, gender, geography and platform. Each post carries engagement counts, a virality flag and eighteen automatically derived rhetorical features. The analyses report several findings:

  • Negative posts dominate the corpus.
  • Israel and UK subsets get far more engagement per post than Arabic-language content.
  • Women's posts go viral proportionally as often as men's.
  • Posting rhythms follow religious and media calendars.

The resource will be available to vetted researchers. It supports stance detection, virality prediction, rhetorical analysis and computational research on digital political memory.

Key points

  • Size: over 142,000 posts were harvested; after cleaning, 70,312 unique posts remain, including 28,031 Arabic and 17,200 English posts with confidently assigned language.
  • Sentiment: labels come from Meltwater's automatic classifier, which reports 83% English accuracy and publishes no Arabic figure. Negative posts outnumber positive ones roughly 11:1 (54,424 vs. 4,827).
  • Engagement: average likes per post are highest for Israel (50.62) and UK (49.08) subsets.
  • Virality caveat: the near-constant ~10% virality rate across Twitter/X subsets partly follows from the definition itself (top 10% within each subset), as the authors acknowledge.
  • Release and limitations: access requires an application. Twitter/X content is shared as tweet IDs only, Facebook content as anonymized feature vectors, and commercial use is prohibited. Hebrew is excluded, and Facebook data is Arabic-only.

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

🎓Northwestern University in QatarDoha · Qatar39 works · 148 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksSocial Sciences · 1,954 worksSociology and Political Science · 626 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 3,066 worksDiscourse analysis · 471 works

Cite

Cite as Wajdi Zaghouani, Mabrouka Bessghaier and Kais Attia. 2026. Nakba Discourse 2025: A Bilingual Social Media Dataset for Collective Trauma Analysis. Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/26i8wdd5eyrt

@inproceedings{zaghouani2026nakba,
  title = {{Nakba Discourse 2025: A Bilingual Social Media Dataset for Collective Trauma Analysis}},
  author = {Wajdi Zaghouani, Mabrouka Bessghaier and Kais Attia},
  booktitle = {Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026},
  year = {2026},
  doi = {10.63317/26i8wdd5eyrt},
  url = {https://doi.org/10.63317/26i8wdd5eyrt}
}

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