Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States
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futureinternet-13-00184.pdf
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3.28 MB
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Adobe PDF
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ffd2c8a78032edb72d04625a2f413944
Author(s)
Sun, Jiachen
Gloor, Peter A.
Date Issued
July 2021
Journal
Future Internet
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sun, Jiachen and Peter A. Gloor. "Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States." Future Internet 13, 7 (July 2021): 184. © 2021 The Authors
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/fi13070184
