# ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection

arXiv: https://arxiv.org/abs/2010.08768 · HTML: https://mlsanity.com/paper/2010.08768/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 15 of 15)

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- [Ta'keed: The First Generative Fact-Checking System for Arabic Claims](https://mlsanity.com/paper/2401.14067/) (2024)
- [AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset](https://mlsanity.com/paper/2105.03143/) (2021)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Comparing Open Arabic Named Entity Recognition Tools](https://mlsanity.com/paper/2205.05857/) (2022)
- [A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection](https://mlsanity.com/paper/2405.03920/) (2024)
- [Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2410.03293/) (2024)
- [Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study](https://mlsanity.com/paper/2301.05863/) (2023)
- [A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data](https://mlsanity.com/paper/2311.08001/) (2023)
- [Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis](https://mlsanity.com/paper/2409.05292/) (2024)

## Abstract (arXiv)

In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-checking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by veracity to support research on misinformation detection, which is one of the major problems faced during a pandemic. ArCOV19-Rumors supports two levels of misinformation detection over Twitter: verifying free-text claims (called claim-level verification) and verifying claims expressed in tweets (called tweet-level verification). Our dataset covers, in addition to health, claims related to other topical categories that were influenced by COVID-19, namely, social, politics, sports, entertainment, and religious. Moreover, we present benchmarking results for tweet-level verification on the dataset. We experimented with SOTA models of versatile approaches that either exploit content, user profiles features, temporal features and propagation structure of the conversational threads for tweet verification.