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

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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).
    5. 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.
    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. 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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. Candio, Paolo & Violato, Mara & Clarke, Philip M & Duch, Raymond & Roope, Laurence SJ, 2023. "Prevalence, predictors and reasons for COVID-19 vaccine hesitancy: Results of a global online survey," Health Policy, Elsevier, vol. 137(C).
    6. Jianfeng Guo & Chao Deng & Fu Gu, 2021. "Vaccinations, Mobility and COVID-19 Transmission," IJERPH, MDPI, vol. 19(1), pages 1-10, December.

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