Towards predicting COVID-19 trends: Feature engineering on social media responses

ABSTRACT

During the course of this pandemic, the use of social media and virtual networks have been at an all-time high. Individuals have used social media to express their thoughts on matters related to the 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 cases. In addition, we found a RMSE score of0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily deaths. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.