Analysing Public Perception and Sentiment on the COVID-19 Booster Dose through Social Media

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has emerged as a severe public health crisis, reshaping societies worldwide since its inception in December 2019. Amidst these challenges, vaccination emerged as a pivotal strategy to curb transmission and restore normalcy. The journey to develop and distribute vaccines encountered obstacles but ultimately led to significant progress. Vaccine hesitancy persists, impacting the uptake of booster shots. Therefore, this research analyses the acceptance of booster shots by examining the tweets that reflect public perceptions. Over 12,000 tweets provided insights into public perceptions, highlighting the need for nuanced approaches to address vaccine hesitancy. Leveraging topic modelling algorithms such as Latent Dirichlet Allocation (LDA), the study identified pertinent topics and keywords, shedding light on variations in booster shot acceptance across different income categories. It further analysed the public acceptance of booster shots using machine learning techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM). The ANN algorithm outperformed the LSTM, which is known for it´s semantic understanding. Persistent vaccine hesitancy remains a formidable challenge in the battle against COVID-19. Middle-income countries admit to the booster shot compared to high-income countries.