COVID-19 Vaccine Side Effect Analysis by Leveraging Social Media: Focusing on Connectivity and Cluster Characteristics of Vaccine Side Effects

Sunguk Yun, Jaekyun Jeong, Jungeun Kim

Abstract: COVID-19, a highly contagious global epidemic, has prompted governments to actively recommend vaccination as a crucial measure to overcome its impact. However, vaccine hesitancy remains a significant challenge, stemming from concerns related to rapid vaccine development, streamlined clinical trials, misinformation, and potential side effects. To address these concerns, an in-depth understanding of COVID-19 vaccine side effects is paramount. This article aims to analyze COVID-19 vaccine side effects using machine learning applied to Twitter, a representative social media platform. Thorough experiments show that we can not only detect officially known COVID-19 vaccine side effects, such as pain and headache but also identify previously unknown COVID-19 vaccine side effects like myocarditis and thrombosis. More importantly, we show that connectivity analysis and cluster analysis can provide a more detailed understanding of vaccine side effects, including differences from conventional text-mining analysis results. This article has the potential to alleviate public anxiety by discovering and analyzing vaccine side effects through social media data analysis. In addition, the proposed method is more important because it can be applied not only to COVID-19 vaccines but also to other side effects related to other medications.