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To Vaccinate or not to Vaccinate? Analyzing $\mathbb{X}$ Power over the Pandemic by Khan, Tanveer; Sohrab, Fahad; Michalas, Antonis; Gabbouj, Moncef is a scholarly article available to read on EtoBox.

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The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creation of vaccines. To halt the COVID-19 pandemic, the world has started preparing for the global vaccine roll-out. In an effort to navigate the immense volume of information about COVID-19, the public has turned to social networks. Among them, $\mathbb{X}$ (formerly Twitter) has played a key role in distributing related information. Most people are not trained to interpret medical research and remain skeptical about the efficacy of new vaccines. Measuring their reactions and perceptions is gaining significance in the fight against COVID-19. To assess the public perception regarding the COVID-19 vaccine, our work applies a sentiment analysis approach, using natural language processing of $\mathbb{X}$ data. We show how to use textual analytics and textual data visualization to discover early insights (for example, by analyzing the most frequently used keywords and hashtags). Furthermore, we look at how people's sentiments vary across the countries. Our results indicate that although the overall reaction to the vaccine is positive, there are a

Author

Khan, Tanveer; Sohrab, Fahad; Michalas, Antonis; Gabbouj, Moncef

Published

2025

Language

EN