Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter
This provides insights into public reactions on social media during a global health crisis, but it is incremental as it applies standard methods to new data without major methodological advances.
The study analyzed 1.9 million English-language Tweets from early 2020 to understand public discourse and sentiment during the COVID-19 pandemic, identifying 11 topics categorized into themes like updates on cases and economic impact, with sentiment analysis revealing fear as dominant across all topics.
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.