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

Oliveira, Francisco Braulio and Haque, Amanul and Mougouei, Davoud ORCID: https://orcid.org/0000-0002-4271-9174 and Evans, Simon and Sichman, Jaime Simao and Singh, Munindar P. (2022) Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach. IEEE Access, 10. pp. 16883-16897.

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 01 Mar 2022 04:58
Last Modified: 01 Mar 2022 04:58
Uncontrolled Keywords: Blogs; COVID-19; COVID-19; Emotion; Emotion recognition; Emotional responses; Media; Media; News; NLP; Pandemics; RoBERTa Model; Social networking (online); Topic Modeling; Twitter
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Identification Number or DOI: https://doi.org/10.1109/ACCESS.2022.3150329
URI: http://eprints.usq.edu.au/id/eprint/47119

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