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. 2023 Jun 14:100253.
doi: 10.1016/j.osnem.2023.100253. Online ahead of print.

Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning

Francisco Bráulio Oliveira  1 , Davoud Mougouei  2 , Amanul Haque  3 , Jaime Simão Sichman  1 , Hoa Khanh Dam  4 , Simon Evans  5 , Aditya Ghose  4 , Munindar P Singh  3
Affiliations

Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning

Francisco Bráulio Oliveira et al. Online Soc Netw Media. .

Abstract

The media has been used to disseminate public information amid the Covid-19 pandemic. However, the Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to the Covid-19 news, we study user comments on the news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employ a deep-learning-based model to identify one of the 6 Ekman's basic emotions, or the absence of emotional expression, in comments to the Covid-19 news, and an implementation of Latent Dirichlet Allocation (LDA) to identify 12 different topics in the news messages. Our analysis finds that while nearly half of the user comments show no significant emotions, negative emotions are more common. Anger is the most common emotion, particularly in the media and comments about political responses and governmental actions in the United States. Joy, on the other hand, is mainly linked to media outlets from the Philippines and news on vaccination. Over time, anger is consistently the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing and occasionally spiking with news of Covid-19 variants, cases, and deaths. Emotions also vary across media outlets, with Fox News having the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness is highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Also, fear is most evident in the comments to the news from The Times of India.

Keywords: Covid-19; Deep learning; Emotion; Media; News; Topic modeling; Twitter.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
News avoidance in Reuters’ Digital News Report, 2022 .
Fig. 2
Fig. 2
The methodology used for studying the emotional responses to the Covid-19 news on Twitter.
Fig. 3
Fig. 3
Coherence scores evaluated for various numbers of topics used in the training of the Latent Dirichlet Allocation (LDA) model. A coherence score of 0.41 was achieved for 12 topics.
Fig. 4
Fig. 4
The prevalence of emotions in user comments.
Fig. 5
Fig. 5
The monthly prevalence of emotions in user comments. For instance, the dark blue line represents the monthly prevalence of user comments with anger as its prevalent emotion. User comments labeled as neutral were not included. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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