부산대학교 도서관

During the COVID-19 pandemic many people used social media to seek information about the disease. In addition, these platforms were used to share news and opinions on issues related to COVID-19, such as vaccines and isolation policies. Extracting useful information for public health from these platforms efficiently poses some challenges due to the characteristics of social networks. Therefore, this article presents a method combining clustering and natural language processing for extracting information about users and their posts on social networks about COVID-19. The results show that the combination of clustering methods and textual analysis can reveal valuable information for public health using data from social networks. Future studies can employ this proposed method in order to get real-time information during future epidemics