Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach
Article
Oliveira, Francisco Braulio, Haque, Amanul, Mougouei, Davoud, Evans, Simon, 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. https://doi.org/10.1109/ACCESS.2022.3150329
| Article Title | Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Oliveira, Francisco Braulio (Author), Haque, Amanul (Author), Mougouei, Davoud (Author), Evans, Simon (Author), Sichman, Jaime Simao (Author) and Singh, Munindar P. (Author) |
| Journal Title | IEEE Access |
| Journal Citation | 10, pp. 16883-16897 |
| Number of Pages | 15 |
| Year | 2022 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3150329 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/9709267 |
| 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 | Blogs; COVID-19; COVID-19; Emotion; Emotion recognition; Emotional responses; Media; Media; News; NLP; Pandemics; RoBERTa Model; Social networking (online); Topic Modeling; Twitter |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
| 461103. Deep learning | |
| Byline Affiliations | Sao Paulo State University, Brazil |
| North Carolina State University, United States | |
| School of Mathematics, Physics and Computing | |
| University of Surrey, United Kingdom | |
| Institution of Origin | University of Southern Queensland |
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