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

Lisa Singh, Leticia Bode, Ceren Budak, Kornraphop Kawintiranon, Colton Padden, Emily Vraga

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)343-366
Number of pages24
JournalJournal of Computational Social Science
Volume3
Issue number2
DOIs
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

Keywords

  • COVID-19
  • Coronavirus
  • Low quality domains
  • Misinformation
  • Twitter conversation

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