Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter

TL;DR

This study analyzes 1.9 million English Tweets during early COVID-19 to identify key topics and sentiment, revealing fear and concern about the pandemic's impact without focus on treatments or symptoms.

Contribution

It applies LDA topic modeling to large-scale Twitter data to uncover dominant themes and sentiment related to COVID-19 in early 2020.

Findings

Identified 11 salient COVID-19 topics on Twitter

Fear was the dominant sentiment across topics

Economic impact and preventive measures were prominent themes

Abstract

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.