ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic
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Abstract (arXiv)
Over the past few months, there were huge numbers of circulating tweets and discussions about Coronavirus (COVID-19) in the Arab region. It is important for policy makers and many people to identify types of shared tweets to better understand public behavior, topics of interest, requests from governments, sources of tweets, etc. It is also crucial to prevent spreading of rumors and misinformation about the virus or bad cures. To this end, we present the largest manually annotated dataset of Arabic tweets related to COVID-19. We describe annotation guidelines, analyze our dataset and build effective machine learning and transformer based models for classification.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
- 2023Detecting and Reasoning of Deleted Tweets before they are Postedcited by 4
- 2022ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccinationcited by 3
- 2021Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twittercited by 1
- 2023Detecting and Reasoning of Deleted Tweets before they are Posted5 May 2023
- 2022ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination17 Jan 2022
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection23 Sep 2021
- 2021Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter9 Jan 2021