IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v3y2020i2d10.1007_s42001-020-00093-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-020-00093-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-020-00093-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junhui Cai & Dan Yang & Wu Zhu & Haipeng Shen & Linda Zhao, 2021. "Network regression and supervised centrality estimation," Papers 2111.12921, arXiv.org.
    2. Yevgeniy Golovchenko, 2020. "Measuring the scope of pro-Kremlin disinformation on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    3. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.
    4. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    5. Hyehyun Hong & Hyun Jee Oh, 2020. "Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    6. Chuhan Wu & Fangzhao Wu & Tao Qi & Wei-Qiang Zhang & Xing Xie & Yongfeng Huang, 2022. "Removing AI’s sentiment manipulation of personalized news delivery," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    7. Leopoldo Fergusson & Carlos Molina, 2020. "Facebook Causes Protests," HiCN Working Papers 323, Households in Conflict Network.
    8. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    9. Casey A. Klofstad & Joseph E. Uscinski & Jennifer M. Connolly & Jonathan P. West, 2019. "What drives people to believe in Zika conspiracy theories?," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-8, December.
    10. Michele Cantarella & Nicolo' Fraccaroli & Roberto Volpe, 2019. "Does fake news affect voting behaviour?," Department of Economics 0146, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    11. Bartosz Wilczek, 2020. "Misinformation and herd behavior in media markets: A cross-national investigation of how tabloids’ attention to misinformation drives broadsheets’ attention to misinformation in political and business," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    12. Ciprian-Octavian Truică & Elena-Simona Apostol, 2022. "MisRoBÆRTa: Transformers versus Misinformation," Mathematics, MDPI, vol. 10(4), pages 1-25, February.
    13. 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.
    14. Uğur Baloğlu, 2021. "Trolls, Pressure and Agenda: The discursive fight on Twitter in Turkey," Media and Communication, Cogitatio Press, vol. 9(4), pages 39-51.
    15. Argyris, Young Anna & Kim, Yongsuk & Roscizewski, Alexa & Song, Won, 2021. "The mediating role of vaccine hesitancy between maternal engagement with anti- and pro-vaccine social media posts and adolescent HPV-vaccine uptake rates in the US: The perspective of loss aversion in," Social Science & Medicine, Elsevier, vol. 282(C).
    16. John Higgins & Tarun Sabarwal, 2023. "Control and spread of contagion in networks with global effects," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 25(6), pages 1149-1187, December.
    17. Yuho Chung & Yiwei Li & Jianmin Jia, 2021. "Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 925-946, September.
    18. Mujtaba Ali Isani, 2021. "Methodological Problems of Using Arabic-Language Twitter as a Gauge for Arab Attitudes Toward Politics and Society," Contemporary Review of the Middle East, , vol. 8(1), pages 22-35, March.
    19. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    20. Nwaibeh, E.A. & Chikwendu, C.R., 2023. "A deterministic model of the spread of scam rumor and its numerical simulations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 111-129.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00093-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.