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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lisa Singh & Leticia Bode & Ceren Budak & Kornraphop Kawintiranon & Colton Padden & Emily Vraga, 2020. "Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams," Journal of Computational Social Science, Springer, vol. 3(2), pages 343-366, November.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00093-6
    DOI: 10.1007/s42001-020-00093-6
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    References listed on IDEAS

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    1. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2019. "Trends in the Diffusion of Misinformation on Social Media," NBER Working Papers 25500, National Bureau of Economic Research, Inc.
    2. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    4. Motta, Matthew & Callaghan, Timothy & Sylvester, Steven, 2018. "Knowing less but presuming more: Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes," Social Science & Medicine, Elsevier, vol. 211(C), pages 274-281.
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    Cited by:

    1. Emilio Ferrara & Stefano Cresci & Luca Luceri, 2020. "Misinformation, manipulation, and abuse on social media in the era of COVID-19," Journal of Computational Social Science, Springer, vol. 3(2), pages 271-277, November.
    2. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    3. Sameera Horawalavithana & Ravindu Silva & Nipuna Weerasekara & N G Kin Wai & Mohamed Nabeel & Buddhini Abayaratna & Charitha Elvitigala & Primal Wijesekera & Adriana Iamnitchi, 2023. "Vaccination trials on hold: malicious and low credibility content on Twitter during the AstraZeneca COVID-19 vaccine development," Computational and Mathematical Organization Theory, Springer, vol. 29(3), pages 448-469, September.
    4. Verónica Israel-Turim & Josep Lluís Micó-Sanz & Miriam Diez Bosch, 2022. "Who Did Spanish Politicians Start Following on Twitter? Homophilic Tendencies among the Political Elite," Social Sciences, MDPI, vol. 11(7), pages 1-19, July.
    5. Katharina Baum & Annika Baumann & Katharina Batzel, 2024. "Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(3), pages 335-355, June.

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