An Evolutionary Clustering Analysis of Social Media Content and Global Infection Rates During the COVID-19 Pandemic

Abstract

<jats:p> This study investigates the impact of global infection rates on social media posts during the COVID-19 pandemic. The study analysed over 179 million tweets posted between March 22 and April 13, 2020 and the global COVID-19 infection rates using evolutionary clustering analysis. Results showed six clusters constructed for each term type, including three-level [Formula: see text]-grams (unigrams, bigrams and trigrams). The frequent occurrences of unigrams (“COVID-19”, “virus”, “government”, “people”, etc.), bigrams (“COVID 19”, “COVID-19 cases”, “times share”, etc.) and trigrams (“COVID 19 crisis”, “things help stop” and “trying times share”) were identified. The results demonstrated that the unigram trends on Twitter were up to about two times and 54 times more common than the bigram terms and trigram terms, respectively. Unigrams like “home” or “need” also became important as these terms reflected the main concerns of people during this period. Taken together, the present findings confirm that many tweets were used to broadcast people’s prevalent topics of interest during the COVID-19 pandemic. Furthermore, the results indicate that the number of COVID-19 infections had a significant effect on all clusters, being strong on 86% of clusters and moderate on 16% of clusters. The downward slope in global infection rates reflected the start of the trending of “social distancing” and “stay at home”. These findings suggest that infection rates have had a significant impact on social media posting during the COVID-19 pandemic. </jats:p>