# Characterizing and Comparing COVID-19 Misinformation Across Languages, Countries and Platforms

arXiv: https://arxiv.org/abs/2010.06455 · HTML: https://mlsanity.com/paper/2010.06455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale](https://mlsanity.com/paper/2308.02068/) (2023)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [Misinformation Span Detection in Videos via Audio Transcripts](https://mlsanity.com/paper/2604.21767/) (2026)

## Abstract (arXiv)

Misinformation/disinformation about COVID-19 has been rampant on social media around the world. In this study, we investigate COVID-19 misinformation/ disinformation on social media in multiple languages - Farsi (Persian), Chinese, and English, about multiple countries - Iran, China, and the United States (US), and on multiple platforms such as Twitter, Facebook, Instagram, Weibo, and WhatsApp. Misinformation, especially about a global pandemic, is a global problem yet it is common for studies of COVID-19 misinformation on social media to focus on a single language, like English, a single country, like the US, or a single platform, like Twitter. We utilized opportunistic sampling to compile 200 specific items of viral and yet debunked misinformation across these languages, countries and platforms emerged between January 1 and August 31. We then categorized this collection based both on the topics of the misinformation and the underlying roots of that misinformation. Our multi-cultural and multilingual team observed that the nature of COVID-19 misinformation on social media varied in substantial ways across different languages/countries depending on the cultures, beliefs/religions, popularity of social media, types of platforms, freedom of speech and the power of people versus governments. We observe that politics is at the root of most of the collected misinformation across all three languages in this dataset. We further observe the different impact of government restrictions on platforms and platform restrictions on content in Iran, China, and the US and their impact on a key question of our age: how do we control misinformation without silencing the voices we need to hold governments accountable?