Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

Lisa Singh (), Leticia Bode, Ceren Budak, Kornraphop Kawintiranon, Colton Padden and Emily Vraga
Additional contact information
Lisa Singh: Georgetown University
Leticia Bode: Georgetown University
Ceren Budak: University of Michigan
Kornraphop Kawintiranon: Georgetown University
Colton Padden: Georgetown University
Emily Vraga: University of Minnesota

Journal of Computational Social Science, 2020, vol. 3, issue 2, No 4, 343-366

Abstract: Abstract This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. We distinguish between high quality health sources, traditional news sources, and low quality misinformation sources. We find that misinformation, in terms of tweets containing URLs from low quality misinformation websites, is shared at a higher rate than tweets containing URLs on high quality health information websites. However, both are a relatively small proportion of the overall conversation. In contrast, news sources are shared at a much higher rate. These findings lead us to analyze the network created by the URLs referenced on the webpages shared by Twitter users. When looking at the combined network formed by all three of the source types, we find that the high quality health information network, the low quality misinformation network, and the news information network are all well connected with a clear community structure. While high and low quality sites do have connections to each other, the connections to and from news sources are more common, highlighting the central brokerage role news sources play in this information ecosystem. Our findings suggest that while low quality URLs are not extensively shared in the COVID-19 Twitter conversation, a well connected community of low quality COVID-19 related information has emerged on the web, and both health and news sources are connecting to this community.

Keywords: COVID-19; Coronavirus; Twitter conversation; Misinformation; Low quality domains (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s42001-020-00093-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00093-6

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-020-00093-6

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().