---
title: "AuRED — paper by Haouari et al. (2024) — summary and ideas"
description: "To support the task, the authors release AuRED, the first Authority-Rumor-Evidence Dataset. 52 references, cited by 2 papers here."
authors: ["Fatima Haouari", "Tamer Elsayed", "Reem Suwaileh"]
year: 2024
venue: "Proceedings of the Second Arabic Natural Language Processing Conference"
source: "https://aclanthology.org/2024.arabicnlp-1.3/"
doi: "10.18653/v1/2024.arabicnlp-1.3"
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/haouari-2024-aured-enabling-arabic-rumor-verification/"
---
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# AuRED: Enabling Arabic Rumor Verification using Evidence from Authorities over Twitter

**Authors:** [Fatima Haouari](https://md.research.elamur.ai/free/en/authors/fatima-haouari/), [Tamer Elsayed](https://md.research.elamur.ai/free/en/authors/tamer-elsayed/) and [Reem Suwaileh](https://md.research.elamur.ai/free/en/authors/reem-suwaileh/) · **Year:** 2024 · **Venue:** [Proceedings of the Second Arabic Natural Language Processing Conference](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2024](https://md.research.elamur.ai/free/en/venues/arabicnlp/2024/)) · **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/2024.arabicnlp-1.3/) · [PDF](https://aclanthology.org/2024.arabicnlp-1.3.pdf) · [DOI 10.18653/v1/2024.arabicnlp-1.3](https://doi.org/10.18653/v1/2024.arabicnlp-1.3) · [OpenAlex](https://openalex.org/W4402671414)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/haouari-2024-aured-enabling-arabic-rumor-verification/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses rumor verification on Arabic Twitter using a new evidence source: the timelines of *authorities*, meaning accounts with the actual knowledge or power to confirm or deny a given claim. Earlier work mostly relied on reply networks, user metadata, or Web pages. The task has two stages: retrieve evidence tweets from the authorities' timelines, then label the rumor as supported, refuted, or unverifiable using only that evidence. To support the task, the authors release AuRED, the first Authority-Rumor-Evidence Dataset. It contains 160 rumors drawn from AuFIN and AuSTR, paired with 692 authority timelines totalling roughly 34k annotated tweets. They benchmark existing fact-checking models, including BM25, mContriever, MLA and KGAT, in two settings: cross-lingual zero-shot (training on FEVER) and in-domain fine-tuning. Retrieval baselines turn out reasonably strong, while verification stays weak however good the retrieval is. Stance detection helps retrieval. The work matters because it frames misinformation checking around trustworthy sources and gives a low-resource language a public benchmark for it.

## Key points
- AuRED's label distribution is 30 supported (18.75%), 64 refuted (40%) and 66 not-enough-info (41.25%), with about 4.33 authorities per rumor and 33,705 authority tweets.
- Of the rumors, 99 come from AuFIN and 61 from AuSTR. All true rumors had to come from AuSTR, because fact-checking sites rarely cover confirmed claims.
- Authorities were found by two annotators working independently, who then met and kept only the candidates they both agreed on. Agreement on the AuRED* subset reached a Cohen's Kappa of 0.67.
- In-domain fine-tuned verification is low: macro-F1 is 0.351 for MLA and 0.371 for KGAT on AuRED, and the strict macro-F1 is lower still. KGAT verifies better even though MLA retrieves better.
- In 38.5% of evidence tweets the evidence is not textual (images or video). The current work leaves this multimodal evidence unused.

## Interesting ideas
- Evidence counts only if it comes from a source entitled to know, so trust is tied to who speaks rather than to text similarity alone.
- The verdict set includes an explicit "not enough information" class, which is the most frequent label here, so abstaining is a first-class outcome rather than a failure.
- Retrieval and verification are scored separately, and the paper shows that the best retriever is not the best verifier.
- An item is kept only when two independent annotators agree after reconciliation, which trades coverage for reliability.
- Detecting whether a source confirms or denies a claim is used as a signal for finding relevant evidence.

## For Mova

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

- **I1** (world): Facts in the world of a text could carry a provenance slot (speaker, and whether that speaker has authority over the topic), so the system can weigh or reject claims explainably.
- **I2** (answer): Readers answering fable or school questions should be able to return "the text does not say" and be scored on it, which discourages confident guessing.
- **I3** (learn): Evaluation should report each pipeline step on its own (tree, frames, world, answer) so that a gain at one stage is not mistaken for end-to-end progress.
- **I4** (learn): This mirrors the witness-parser agreement used for silver data, and the paper's Kappa reporting suggests also publishing an agreement score for each silver batch.
- **I5** (frames): Tagging speech acts such as denial, confirmation, or a neutral mention on frames could help select the sentences that actually bear on a question.

*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

- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 141 works · 14,175 citations
- [Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/) — University, Doha, Qatar · 48 works · 1,026 citations

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## Cite

Cite as: Fatima Haouari, Tamer Elsayed and Reem Suwaileh. 2024. AuRED: Enabling Arabic Rumor Verification using Evidence from Authorities over Twitter. Proceedings of the Second Arabic Natural Language Processing Conference.

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

```bibtex
@inproceedings{haouari2024aured,
  title = {{AuRED: Enabling Arabic Rumor Verification using Evidence from Authorities over Twitter}},
  author = {Fatima Haouari, Tamer Elsayed and Reem Suwaileh},
  booktitle = {Proceedings of the Second Arabic Natural Language Processing Conference},
  year = {2024},
  doi = {10.18653/v1/2024.arabicnlp-1.3},
  url = {https://doi.org/10.18653/v1/2024.arabicnlp-1.3}
}
```

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## References

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*Updated 1 November 2026*