THE ROLE OF SOCIAL MEDIA ANALYTICS IN GOVERNMENT DECISION-MAKING: A MACHINE LEARNING APPROACH USING COVID-19 VACCINATION TWEETS

Social media has emerged as a vital platform for expressing public opinion and disseminating information. During the COVID-19 pandemic, governments worldwide leveraged social media platforms to inform citizens about policies, preventive measures, and vaccination campaigns. Analyzing these vast amounts of data using machine learning and social media analytics (SMA) provides crucial insights to support policy development and strategic decision-making. This study explores the role of SMA in government decision-making during the COVID-19 pandemic by evaluating sentiment classification on English-language tweets related to vaccination. We created a novel COVID-19 vaccine-related tweet dataset and applied six machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), XGBoost, Decision Tree (DT), Naïve Bayes (NB), and Logistic Regression (LR)—to classify tweet sentiments. Performance metrics, including accuracy, precision, recall, and F1-score were used to compare model performance. The results indicate that the Random Forest model outperformed others, achieving the highest accuracy, precision, recall, and F1-score. This demonstrates the potential of SMA combined with machine learning in capturing public sentiment and supporting informed government responses during public health crises.