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Author |
Antone Evans Jr.; Yingyuan Yang; Sunshin Lee |
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Title |
Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Issue |
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Pages |
792-807 |
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Keywords |
Big Data Analysis, Machine Learning, COVID-19, Twitter |
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Abstract |
During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases. |
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Address |
University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title  |
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Edition |
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ISSN |
978-1-949373-61-5 |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
aevan7@uis.edu |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2374 |
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