The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing
The emotional impact of the COVID-19 pandemic and ensuing social restrictions has been profound, with widespread negative effects on mental health. We made use of the natural language processing and large-scale Twitter data to explore this in depth, identifying emotions in COVID-19 news content and...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | Human Behavior and Emerging Technologies |
| Online Access: | http://dx.doi.org/10.1155/2023/7283166 |
| _version_ | 1797869091624583168 |
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| author | Simon L. Evans Rosalind Jones Erkan Alkan Jaime Simão Sichman Amanul Haque Francisco Bráulio Silva de Oliveira Davoud Mougouei |
| author_facet | Simon L. Evans Rosalind Jones Erkan Alkan Jaime Simão Sichman Amanul Haque Francisco Bráulio Silva de Oliveira Davoud Mougouei |
| author_sort | Simon L. Evans |
| collection | DOAJ |
| description | The emotional impact of the COVID-19 pandemic and ensuing social restrictions has been profound, with widespread negative effects on mental health. We made use of the natural language processing and large-scale Twitter data to explore this in depth, identifying emotions in COVID-19 news content and user reactions to it, and how these evolved over the course of the pandemic. We focused on major UK news channels, constructing a dataset of COVID-related news tweets (tweets from news organisations) and user comments made in response to these, covering Jan 2020 to April 2021. Natural language processing was used to analyse topics and levels of anger, joy, optimism, and sadness. Overall, sadness was the most prevalent emotion in the news tweets, but this was seen to decline over the timeframe under study. In contrast, amongst user tweets, anger was the overall most prevalent emotion. Time epochs were defined according to the time course of the UK social restrictions, and some interesting effects emerged regarding these. Further, correlation analysis revealed significant positive correlations between the emotions in the news tweets and the emotions expressed amongst the user tweets made in response, across all channels studied. Results provide unique insight onto how the dominant emotions present in UK news and user tweets evolved as the pandemic unfolded. Correspondence between news and user tweet emotional content highlights the potential emotional effect of online news on users and points to strategies to combat the negative mental health impact of the pandemic. |
| first_indexed | 2024-04-10T00:06:04Z |
| format | Article |
| id | doaj.art-dcfa774ae796489f927627af2846f467 |
| institution | Directory Open Access Journal |
| issn | 2578-1863 |
| language | English |
| last_indexed | 2024-04-10T00:06:04Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Human Behavior and Emerging Technologies |
| spelling | doaj.art-dcfa774ae796489f927627af2846f4672023-03-17T00:00:03ZengHindawi-WileyHuman Behavior and Emerging Technologies2578-18632023-01-01202310.1155/2023/7283166The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language ProcessingSimon L. Evans0Rosalind Jones1Erkan Alkan2Jaime Simão Sichman3Amanul Haque4Francisco Bráulio Silva de Oliveira5Davoud Mougouei6Faculty of Health and Medical SciencesFaculty of Health and Medical SciencesInstitute of Health and WellbeingLaboratório de Técnicas InteligentesSocial AI Lab (Engineering Building 2Laboratório de Técnicas InteligentesSchool of Information TechnologyThe emotional impact of the COVID-19 pandemic and ensuing social restrictions has been profound, with widespread negative effects on mental health. We made use of the natural language processing and large-scale Twitter data to explore this in depth, identifying emotions in COVID-19 news content and user reactions to it, and how these evolved over the course of the pandemic. We focused on major UK news channels, constructing a dataset of COVID-related news tweets (tweets from news organisations) and user comments made in response to these, covering Jan 2020 to April 2021. Natural language processing was used to analyse topics and levels of anger, joy, optimism, and sadness. Overall, sadness was the most prevalent emotion in the news tweets, but this was seen to decline over the timeframe under study. In contrast, amongst user tweets, anger was the overall most prevalent emotion. Time epochs were defined according to the time course of the UK social restrictions, and some interesting effects emerged regarding these. Further, correlation analysis revealed significant positive correlations between the emotions in the news tweets and the emotions expressed amongst the user tweets made in response, across all channels studied. Results provide unique insight onto how the dominant emotions present in UK news and user tweets evolved as the pandemic unfolded. Correspondence between news and user tweet emotional content highlights the potential emotional effect of online news on users and points to strategies to combat the negative mental health impact of the pandemic.http://dx.doi.org/10.1155/2023/7283166 |
| spellingShingle | Simon L. Evans Rosalind Jones Erkan Alkan Jaime Simão Sichman Amanul Haque Francisco Bráulio Silva de Oliveira Davoud Mougouei The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing Human Behavior and Emerging Technologies |
| title | The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing |
| title_full | The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing |
| title_fullStr | The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing |
| title_full_unstemmed | The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing |
| title_short | The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing |
| title_sort | emotional impact of covid 19 news reporting a longitudinal study using natural language processing |
| url | http://dx.doi.org/10.1155/2023/7283166 |
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