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.

China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo

“…The present study used the LDA for topic analysis of negative sentiment posts. The LDA is a popular topic-mining method (Xie et al, 2021 ; Ogbuokiri et al, 2022 ; Oliveira et al, 2022 ; Wang H. et al, 2022 ). However, it was difficult to determine the optimal number of topics.…”

“…Several studies have conducted emotion analyses on social media using different emotion-classification methods. Oliveira et al ( 2022 ) analyzed tweets labeled with the emotions of anger, sadness, optimism, and joy. Ye et al ( 2020 ) adopted Ekman's six basic emotions.…”

Comparando os posicionamentos a favor/contra a vacinação COVID nos Estados Unidos da América e no Brasil

“…A extrac ¸ão da percepc ¸ão da populac ¸ão sobre a COVID-19 usando dados do Twitter é uma área bastante ativa [Tao et al 2020, Xue et al 2020, Oliveira et al 2022, onde o LDA é a técnica mais popular. Diferenc ¸as nas percepc ¸ões emocionais sobre a COVID-19 entre os EUA e no Brasil é desenvolvida em [Garcia and Berton 2021].…”

“…Diferentes trabalhos têm contribuído à compreensão da percepc ¸ão de aspectos ligados à COVID expressos em redes sociais, principalmente o Twitter, principalmente com a técnica de modelagem de tópicos LDA [Xue et al 2020, Tao et al 2020, Oliveira et al 2022. Uma comparac ¸ão entre as percepc ¸ões e sentimentos sobre a COVID-19 nos EUA e no Brasil foi desenvolvida em [Garcia and Berton 2021].…”

Research on COVID-19 network public opinion events based on topic model and community detection algorithm

“…In order to study the influence of images on the spread of online public opinion, Seltzer et al took the online public opinion of the Zika virus epidemic as an example, and analysed the spread of image public opinion on the Instagram platform [6]. Oliveira et al used the LDA model to identify user text topics on Twitter, and used an optimized BERT pre-training model to identify the sentiment, and conducted topic-sentiment analysis on the Twitter network public opinion of COVID-19 [7]. Singh et al used the LDA algorithm for topic modeling, and based on the NRC sentiment vocabulary, they used Syuzhet software package to perform sentiment topic analysis on the food network public opinion on Twitter [8].…”