Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning.

This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth.