Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model
IntroductionTo protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1105383/full |
| _version_ | 1827345914248822784 |
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| author | Aisha Aldosery Robert Carruthers Karandeep Kay Christian Cave Paul Reynolds Patty Kostkova |
| author_facet | Aisha Aldosery Robert Carruthers Karandeep Kay Christian Cave Paul Reynolds Patty Kostkova |
| author_sort | Aisha Aldosery |
| collection | DOAJ |
| description | IntroductionTo protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.MethodsIn this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.ResultsThe results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.DiscussionWhile much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public’s stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest. |
| first_indexed | 2024-03-07T23:20:38Z |
| format | Article |
| id | doaj.art-246c336118d540558e15925c5821d5a1 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-07T23:20:38Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-246c336118d540558e15925c5821d5a12024-02-21T05:50:56ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-02-011210.3389/fpubh.2024.11053831105383Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic modelAisha Aldosery0Robert Carruthers1Karandeep Kay2Christian Cave3Paul Reynolds4Patty Kostkova5Centre for Digital Public Health in Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United KingdomDepartment of Computer Science, University College London, London, United KingdomDepartment of Computer Science, University College London, London, United KingdomDepartment of Computer Science, University College London, London, United KingdomDepartment of Computer Science, University College London, London, United KingdomCentre for Digital Public Health in Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United KingdomIntroductionTo protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.MethodsIn this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.ResultsThe results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.DiscussionWhile much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public’s stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1105383/fullCOVID-19sentiment analysisTwitterpublic responseUnited Kingdomtopic modeling |
| spellingShingle | Aisha Aldosery Robert Carruthers Karandeep Kay Christian Cave Paul Reynolds Patty Kostkova Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model Frontiers in Public Health COVID-19 sentiment analysis public response United Kingdom topic modeling |
| title | Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model |
| title_full | Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model |
| title_fullStr | Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model |
| title_full_unstemmed | Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model |
| title_short | Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model |
| title_sort | enhancing public health response a framework for topics and sentiment analysis of covid 19 in the uk using twitter and the embedded topic model |
| topic | COVID-19 sentiment analysis public response United Kingdom topic modeling |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1105383/full |
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