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More Active Internet-Search on Google and Twitter Posting for COVID-19 Corresponds with Lower Infection Rate in the 50 U.S. States


 As the novel coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 2.5 million total confirmed cases up to now (June 2, 2020). In this work, we investigate the predictive power of 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 the earlier the collective awareness on Twitter/Google in a state, the lower is the infection rate.