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Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States

Author

Listed:
  • Jiachen Sun

    (MIT Center for Collective Intelligence, Cambridge, MA 02140, USA
    School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China)

  • Peter A. Gloor

    (MIT Center for Collective Intelligence, Cambridge, MA 02140, USA)

Abstract

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country, with more than 34.1 million total confirmed cases up to 1 June 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet searches and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with a lower infection rate. Lastly, we demonstrate that correlations between online social media and search trends are sensitive to time, mainly due to the attention shifting of the public.

Suggested Citation

  • Jiachen Sun & Peter A. Gloor, 2021. "Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States," Future Internet, MDPI, vol. 13(7), pages 1-13, July.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:7:p:184-:d:597369
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    References listed on IDEAS

    as
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    2. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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