Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach

Abstract

Media has played an important role in public information on COVID-19. But distressing news, e.g., COVID-19 death tolls, may trigger negative emotions in public, discouraging them from following the news, which, in turn, can limit the effectiveness of the media. To understand people’s emotional response to the COVID-19 news, we have investigated the prevalence of basic human emotions in around 19 million user responses to 1.7 million COVID-19 news posts on Twitter from (English-speaking) media across 12 countries from January 2020 to April 2021. We have used Latent Dirichlet Allocation (LDA) to identify news themes on Twitter. Also, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model was used to identify emotions in the tweets. Our analysis of the Twitter data revealed that anger was the most prevalent emotion in user responses to the news coverage of COVID-19. That was followed by sadness, optimism, and joy, steadily over the period of the study. The prevalence of anger (in user responses) was higher for the news about authorities and politics while optimism and joy were more prevalent for the news about vaccination and educational impacts of COVID-19 respectively. The prevalence of sadness in user responses, however, was the highest for the news about COVID-19 cases and deaths and the impacts on the families, mental health, jails, and nursing homes. We also observed a higher level of anger in the user responses to the (COVID-19) news posted by the USA media accounts (e.g., CNN Politics, Fox News, MSNBC). Optimism, on the other hand, was found to be the highest for Filipino media accounts.

Keywords

• COVID-19
• media
• news
• emotion
• Twitter
• RoBERTa model