| Title |
Tweets and PCR Test-based Analysis and Prediction of Social Response to a Future Pandemic. A Case Study |
| Authors |
(Ghulam Musa Raza) ; (Byung-Seo Kim) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.1.72 |
| Keywords |
COVID-19; Predictive social analysis; Polymerase chain reaction (PCR); Natural language processing (NLP) |
| Abstract |
The COVID-19 pandemic has greatly affected our society badly. It has been a subject of discussion since 2019 due to the increased prevalence of social media and its extensive use, and it has been a source of tension, fear, and disappointment for people all over the world. In this research, we took data from COVID-19 tweets from 10 different regions from July 25, 2020, to August 29, 2020. Using the well-known word embedding technique count-vectorizer, we experimented with different machine learning classifiers on data to train deep neural networks to improve the accuracy of predicted opinions with a low elapsed time. In addition, we collected PCR results from these regions for the same time interval. We compared the opinions in the form of positive or negative responses with the results of the PCR tests per million people. With the help of the results, We figured out a real-time international measure to detect these regions’ behaviors for any future pandemic. If we know how a region thinks about an upcoming pandemic, then we can predict the region’s real-time behavior for the particular pandemic. This would happen if we had past case studies to compare, like in our proposed research. |