# A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

arXiv: https://arxiv.org/abs/2109.11372 · HTML: https://mlsanity.com/paper/2109.11372/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 73 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 30 of 39)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness](https://mlsanity.com/paper/1808.05542/) (2018)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Detecting East Asian Prejudice on Social Media](https://mlsanity.com/paper/2005.03909/) (2020)
- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)
- [Argotario: Computational Argumentation Meets Serious Games](https://mlsanity.com/paper/1707.06002/) (2017)
- [Fully Automated Fact Checking Using External Sources](https://mlsanity.com/paper/1710.00341/) (2017)
- [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2001.08546/) (2020)
- [The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News](https://mlsanity.com/paper/1806.07516/) (2018)
- [Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection](https://mlsanity.com/paper/1910.09982/) (2019)
- [Prta: A System to Support the Analysis of Propaganda Techniques in the News](https://mlsanity.com/paper/2005.05854/) (2020)
- [A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017](https://mlsanity.com/paper/2005.01803/) (2020)
- [SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums](https://mlsanity.com/paper/1906.01727/) (2019)

## Built on by (most cited first, 5 of 5)

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media](https://mlsanity.com/paper/2204.03506/) (2022)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)

## Abstract (arXiv)

While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis of Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.