Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis

Abstract

This study examines social media content to identify adverse effects of COVID-19 vaccination as perceived by the public. Existing studies did not categorize tweets on vaccine adverse effects as personal experience, informative, or advice-seeking. Authors manually classified tweets into categories and used the data to train four machine learning models. LSTM algorithm yielded the highest accuracy of 90.13%. The LSTM model with GloVe embedding was determined to be most suitable. This research aims to fill a research gap and increase public awareness of COVID-19 vaccine side effects. The study highlights the importance of analyzing social media content to better understand public perception of vaccines.