Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak
Not on the map. Why?
Abstract (arXiv)
As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying low-credibility information, although the majority of volume is generated by likely humans. Some of these accounts appear to amplify low-credibility sources in a coordinated fashion.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
6 papers.
- 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
- 2021Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detectioncited by 15
- 2025AI Agent Behavioral Sciencecited by 6
- 2022QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Mediacited by 2
- 2021The Evolution of Rumors on a Closed Platform during COVID-19cited by 1
- 2021Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectivescited by 1
- 2025AI Agent Behavioral Science4 Jun 2025
- 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets26 Apr 2022
- 2022QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media8 Mar 2022
- 2021Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives20 Jul 2021
- 2021The Evolution of Rumors on a Closed Platform during COVID-1928 Apr 2021
- 2021Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection17 Feb 2021