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Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States
| Content Provider | MDPI |
|---|---|
| Author | Sun, Jiachen Gloor, Peter |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 184 |
| e-ISSN | 19995903 |
| DOI | 10.3390/fi13070184 |
| Journal | Future Internet |
| Issue Number | 7 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-20 |
| Access Restriction | Open |
| Subject Keyword | Future Internet Information and Library Science Online Social Media Prediction Covid-19 Prediction Twitter Google Trends |
| Content Type | Text |
| Resource Type | Article |
