Abstract
The coronavirus has caused significant disruption to people's everyday lives, altering how people live, work, and study. The Kingdom of Saudi Arabia (KSA) reacted very quickly to suppress the spread of the virus even before the first case of COVID-19 was confirmed in the country. In the education sector, all face-to-face activities at public and private schools and universities were suspended, as they switched from traditional to distance learning for the entire 2020 academic year. This study collected 1,846,285 tweets to analyze the public's dynamic opinions towards distance education in the KSA during the 2020 academic year. Several classical machine-learning models and deep-learning models, including ensemble random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), multinomial naive Bayes (MNB), convolutional neural network (CNN), and long short-term memory (LSTM), were tested on this data, and the best-performing models were selected to analyze the public stance towards distance education. Additionally, I correlated my analysis with the major events that were announced by the Ministry of Education (MOE). I observed that people in the KSA took some time to react and express their stances at the start of the academic year. Regarding the news, I observed that any exam-related topic attracted high engagement. In-favor stances increased when news headlines covered the topic of exams compared to other topics. The results show that the primary Saudi public stance favored distance education during the 2020 academic year.