mlsanityData to 7 Oct 2026

ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination

arXiv 2201.06496 ↗First posted 17 Jan 2022cs.CL cs.SIcited by 3

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Abstract (arXiv)

The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about the COVID-19 pandemic and at the same time to disseminate information. The content in social media consisted not only health related advises, plans, and informative news from policy makers, but also contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make actionable decisions (e.g., debunking rumors, or taking certain measures for traveling). To address this challenge, we develop and publicly release the first largest manually annotated Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign, covering many countries in the Arab region. The dataset is enriched with different layers of annotation, including, (i) Informativeness (more vs. less importance of the tweets); (ii) fine-grained tweet content types (e.g., advice, rumors, restriction, authenticate news/information); and (iii) stance towards vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed in-depth analysis of the data, exploring the popularity of different vaccines, trending hashtags, topics and presence of offensiveness in the tweets. We studied the data for individual types of tweets and temporal changes in stance towards vaccine. We benchmarked the ArCovidVac dataset using transformer architectures for informativeness, content types, and stance detection.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 64 references that are in the map. First posted on arXiv 17 Jan 2022.

Builds on

27 of 64 references.

References · 27

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  2. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
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15 more
  1. 2020An Exploratory Study of COVID-19 Misinformation on Twittercited by 33
  2. 2020Stance Prediction and Claim Verification: An Arabic Perspectivecited by 25
  3. 2020ktrain: A Low-Code Library for Augmented Machine Learningcited by 23
  4. 2021Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake Newscited by 23
  5. 2020SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemiccited by 16
  6. 2020Machine Generation and Detection of Arabic Manipulated and Fake Newscited by 16
  7. 2020GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Informationcited by 15
  8. 2020ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detectioncited by 15
  9. 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
  10. 2020Large Arabic Twitter Dataset on COVID-19cited by 10
  11. 2020ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networkscited by 7
  12. 2020Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19cited by 5
  13. 2020ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemiccited by 4
  14. 2020Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpuscited by 3
  15. 2020Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreakcited by 3

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Textcited by 19
  2. 2023Detecting and Reasoning of Deleted Tweets before they are Postedcited by 4
  3. 2024Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptationcited by 1