Sun, J.; Gloor, P.A. Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States. Future Internet2021, 13, 184.
Sun, J.; Gloor, P.A. Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States. Future Internet 2021, 13, 184.
Sun, J.; Gloor, P.A. Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States. Future Internet2021, 13, 184.
Sun, J.; Gloor, P.A. Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States. Future Internet 2021, 13, 184.
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 June 1, 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 predictive 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 search and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with 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.
Keywords
Online Social Media prediction, Covid-19 prediction, Twitter, Google Trends
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
