IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i7p3843-d531074.html
   My bibliography  Save this article

The Role of Conspiracy Theories in the Spread of COVID-19 across the United States

Author

Listed:
  • Fu Gu

    (Center of Engineering Management, Polytechnic Institute, Zhejiang University, Hangzhou 310027, China
    Department of Industrial and System Engineering, Zhejiang University, Hangzhou 310027, China
    National Institute of Innovation Management, Zhejiang University, Hangzhou 310027, China)

  • Yingwen Wu

    (Department of Industrial and System Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xinyu Hu

    (Department of Industrial and System Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jianfeng Guo

    (Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, China)

  • Xiaohan Yang

    (Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
    School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xinze Zhao

    (Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
    School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) inspires various conspiracy theories, which could divert public attention, alter human behaviors, and consequently affect the spread of the pandemic. Here we estimate the relation of the online attention on COVID-19-related conspiracy theories to human mobility, as well as to the numbers of confirmed COVID-19 cases, during 14 March 2020 to 28 August 2020. We observe that the online attention to COVID-19 conspiracy theories is significantly and negatively related to human mobility, but its negative impact is noticeably less than those of the attention to official information and personal protection measures. Since human mobility significantly promotes the spread of COVID-19, the attention to official information and personal protection measures lowers COVID-19 cases by 16.16% and 9.41%, respectively, while attention to conspiracy theories only reduces the COVID-19 cases by 6.65%. In addition, we find that in the states with higher online attention to COVID-19 conspiracy theories, the negative relation of the attention to conspiracy theories is much weaker than that in states where there is less concern about conspiracies. This study stresses the necessity of restricting the online transmission of unfounded conspiracy theories during a pandemic.

Suggested Citation

  • Fu Gu & Yingwen Wu & Xinyu Hu & Jianfeng Guo & Xiaohan Yang & Xinze Zhao, 2021. "The Role of Conspiracy Theories in the Spread of COVID-19 across the United States," IJERPH, MDPI, vol. 18(7), pages 1-14, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3843-:d:531074
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/7/3843/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/7/3843/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    2. Dave, Dhaval M. & Friedson, Andrew I. & Matsuzawa, Kyutaro & Sabia, Joseph J. & Safford, Samuel, 2020. "Black Lives Matter Protests, Social Distancing, and COVID-19," IZA Discussion Papers 13388, Institute of Labor Economics (IZA).
    3. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    4. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    5. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    7. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    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. Jaesun Wang & Seoyong Kim, 2021. "The Paradox of Conspiracy Theory: The Positive Impact of Beliefs in Conspiracy Theories on Preventive Actions and Vaccination Intentions during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(22), pages 1-27, November.
    2. Dmitry V. Boguslavsky & Natalia P. Sharova & Konstantin S. Sharov, 2022. "Public Policy Measures to Increase Anti-SARS-CoV-2 Vaccination Rate in Russia," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    3. Marilena Mousoulidou & Andri Christodoulou & Michailina Siakalli & Marios Argyrides, 2023. "The Role of Conspiracy Theories, Perceived Risk, and Trust in Science on COVID-19 Vaccination Decisiveness: Evidence from Cyprus," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    4. Yeon-Jun Choi & Julak Lee & Seung Yeop Paek, 2022. "Public Awareness and Sentiment toward COVID-19 Vaccination in South Korea: Findings from Big Data Analytics," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    5. Jianfeng Guo & Chao Deng & Fu Gu, 2021. "Vaccinations, Mobility and COVID-19 Transmission," IJERPH, MDPI, vol. 19(1), pages 1-10, December.

    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. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
    2. Nikkinen, Jussi & Rothovius, Timo, 2019. "The EIA WPSR release, OVX and crude oil internet interest," Energy, Elsevier, vol. 166(C), pages 131-141.
    3. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "Dynamic linkages between international oil price, plastic stock index and recycle plastic markets in China," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 167-179.
    4. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
    5. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    6. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    7. Damian S. Damianov & Xiangdong Wang & Cheng Yan, 2021. "Google Search Queries, Foreclosures, and House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 63(2), pages 177-209, August.
    8. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    9. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
    10. Qu, Hui & Li, Guo, 2023. "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, vol. 119(C).
    11. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    12. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    13. Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.
    14. Sergiy Saydometov & Sanjiv Sabherwal & Ramya Rajajagadeesan Aroul, 2020. "Sentiment and its asymmetric effect on housing returns," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 580-600, October.
    15. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Attention to oil prices and its impact on the oil, gold and stock markets and their covariance," Energy Economics, Elsevier, vol. 120(C).
    16. Li, Sufang & Zhang, Hu & Yuan, Di, 2019. "Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests," Energy Economics, Elsevier, vol. 84(C).
    17. Derrick W. H. Fung & Charles C. Yang & Jason J. H. Yeh, 2024. "The market price to embedded value gap: an analysis of European life insurers," Review of Quantitative Finance and Accounting, Springer, vol. 62(1), pages 69-96, January.
    18. Chi Dong & Hooi Hooi Lean & Zamri Ahmad & Wing-Keung Wong, 2019. "The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    19. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    20. Bai, Chenjiang & Duan, Yuejiao & Liu, Congya & Qiu, Leiju, 2022. "International taxation sentiment and COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 63(C).

    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:gam:jijerp:v:18:y:2021:i:7:p:3843-:d:531074. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.