To Vaccinate or not to Vaccinate? Analyzing $\mathbb{X}$ Power over the Pandemic

• URL: http://arxiv.org/abs/2503.02563v1
• Date: Tue, 04 Mar 2025 12:40:49 GMT
• Title: To Vaccinate or not to Vaccinate? Analyzing $\mathbb{X}$ Power over the Pandemic
• Authors: Tanveer Khan, Fahad Sohrab, Antonis Michalas, Moncef Gabbouj,
• Abstract summary: To halt the COVID-19 pandemic, the world has started preparing for the global vaccine roll-out.<n>Among them, $mathbbX$ (formerly Twitter) has played a key role in distributing related information.<n>Our work applies a sentiment analysis approach, using natural language processing of $mathbbX$ data.
• Score: 13.944483610447536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 also negative sentiments associated with the tweets, especially when examined at the country level. Additionally, from the extracted tweets, we manually labeled 100 tweets as positive and 100 tweets as negative and trained various One-Class Classifiers (OCCs). The experimental results indicate that the S-SVDD classifiers outperform other OCCs.

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