---
title: "ArCOV19-Rumors — paper by Haouari et al. (2021)"
description: "Arabic Twitter filled with false COVID-19 claims during the early pandemic, and few annotated Arabic… 32 references, cited by 17 papers here."
authors: ["Fatima Haouari", "Maram Hasanain", "Reem Suwaileh", "Tamer Elsayed"]
year: 2021
venue: "Proceedings of the Sixth Arabic Natural Language Processing Workshop"
source: "https://aclanthology.org/2021.wanlp-1.8/"
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-2021-arcov19-rumors-arabic-covid-19/"
---
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# ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection

**Authors:** [Fatima Haouari](https://md.research.elamur.ai/free/en/authors/fatima-haouari/), [Maram Hasanain](https://md.research.elamur.ai/free/en/authors/maram-hasanain/), [Reem Suwaileh](https://md.research.elamur.ai/free/en/authors/reem-suwaileh/) and [Tamer Elsayed](https://md.research.elamur.ai/free/en/authors/tamer-elsayed/) · **Year:** 2021 · **Venue:** [Proceedings of the Sixth Arabic Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2021](https://md.research.elamur.ai/free/en/venues/wanlp/2021/)) · **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/2021.wanlp-1.8/) · [PDF](https://aclanthology.org/2021.wanlp-1.8.pdf) · [OpenAlex](https://openalex.org/W3093262286)

**Citations:** 18 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/haouari-2021-arcov19-rumors-arabic-covid-19/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic Twitter filled with false COVID-19 claims during the early pandemic, and few annotated Arabic resources existed for studying the problem. The authors built ArCOV19-Rumors as an annotated layer on top of ArCOV-19, an existing Arabic COVID-19 tweet collection that includes propagation networks. They gathered verified claims, mostly from the Arabic fact-checkers Fatabyyano and Misbar. To balance the false claims, they added true claims from health authorities and translated items from English fact-checkers. For each claim they built Boolean search queries by hand, filtered the results manually for relevance, and labelled tweets by whether they express or deny the claim. That stance let a claim's veracity pass down to its tweets. The resource supports two tasks: verifying a free-text claim and verifying a claim stated in a single tweet. It also covers topics beyond health, such as politics, sports and religion. In tweet-level benchmarks, the pretrained transformers did best, with the Twitter-trained MARBERT reaching about 0.74 macro-F1. The dataset gives Arabic misinformation research a manually labelled benchmark that is aware of how tweets spread.

## Key points
- The dataset has 138 verified claims and about 9.4K manually labelled relevant tweets (true / false / other), covering 27 January to the end of April 2020.
- Collection started from 162 candidate claims (113 false, 49 true). Boolean retrieval followed by manual filtering produced 14,472 relevant tweets, with the filtering step removing over half of everything the queries returned.
- Claim categories are health, social, politics, sports, entertainment and religious.
- Four baselines were tested: Bi-GCN (reply-tree graph model), PPC-RNN+CNN (user-profile time series), AraBERT and MARBERT. Results were averaged over 5 folds × 5 random seeds and compared against a majority baseline, and MARBERT scored best at about 0.74 macro-F1.
- The release includes the claims, tweet IDs with labels, retweet and full reply-thread IDs, annotation guidelines and the baseline folds.

## Interesting ideas
- Annotators label truth once per claim and then judge only whether each tweet affirms or denies it, so veracity is inherited by many tweets cheaply.
- Search queries were enriched with the different surface words used for one concept (two Arabic terms for "disinfectant", plus "Trump" or "US president"), so lexical variation did not hide relevant posts.
- Relevance was deliberately kept broader than assertion, so questions, advice, opinions and sarcasm about a claim count as on-topic even though they do not state it.
- The reported scores average several random seeds within each fold and sit next to a trivial majority baseline, which makes small differences easier to trust.
- MARBERT, pretrained on tweets, beat the news-trained AraBERT, showing that matching pretraining text to the target domain can matter more than model choice.

## For Mova

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

- **I1** (learn): Silver data could attach a truth value to one canonical proposition and spread it to sentences whose frames entail or negate that proposition.
- **I2** (world): WordNet/Wikidata synonym and alias sets could map varied mentions onto one world entity before facts are compared.
- **I3** (frames): The speech-act and irony layer should separate "talks about P" from "asserts P" before any truth value is assigned to a frame.
- **I4** (learn): Parser and reader evaluations could report seed-averaged scores alongside a majority or random control as a standard negative control.
- **I5** (tree): gparse could be pretrained or fine-tuned on text close to the target genre (e.g., school texts, fables) rather than generic news treebanks.

*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

- [Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/) — University, Doha, Qatar · 53 works · 1,027 citations

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## Cite

Cite as: Fatima Haouari, Maram Hasanain, Reem Suwaileh and Tamer Elsayed. 2021. ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection. Proceedings of the Sixth Arabic Natural Language Processing Workshop.

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

```bibtex
@inproceedings{haouari2021arcov19,
  title = {{ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection}},
  author = {Fatima Haouari, Maram Hasanain, Reem Suwaileh and Tamer Elsayed},
  booktitle = {Proceedings of the Sixth Arabic Natural Language Processing Workshop},
  year = {2021},
  url = {https://aclanthology.org/2021.wanlp-1.8/}
}
```

- [← previous Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation](https://md.research.elamur.ai/free/en/papers/abuzayed-2021-sarcasm-sentiment-detection-arabic-tweets/)
- [next → Infusing Finetuning with Semantic Dependencies](https://md.research.elamur.ai/free/en/papers/wu-2021-infusing-finetuning-semantic-dependencies/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/6/)

## References

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*Updated 1 November 2026*