Visualizing Change and Correlation of Topics With LDA and Agglomerative Clustering on COVID-19 Vaccine Tweets

Abstract

More than three years have passed since the COVID-19 outbreak. The end of the pandemic is in sight, and even some countries have declared COVID-19 as endemic. During the pandemic, COVID-19 vaccination is essential to controlling the situation in many countries. Monitoring opinions on social media is important to understand the public reception of the COVID-19 vaccine. In this study, we combine LDA with agglomerative clustering to investigate the reactions of people from various countries during different periods for approximately one year after a few countries officially approved vaccination. Agglomerative clustering is performed on the latent topics based on the Hellinger distance for measuring the distance between the distributions of the latent topic. As a result, the proposed methods can show relative closeness among the topics and visualize the topic changes over time. The analysis shows that the significant focuses of tweets in the four phases with an interval of three months are the effectiveness of the vaccine, vaccine booster, Delta variant, and vaccine for children, which coincide with the timeline of the critical COVID events announced by WHO.

Keywords

• LDA
• COVID-19
• vaccine
• agglomerative clustering