Kang, X. and Stamolampros, P. orcid.org/0000-0001-8143-7244 (2024) Unveiling public perceptions at the beginning of lockdown: an application of structural topic modeling and sentiment analysis in the UK and India. BMC Public Health, 24. 2832. ISSN 1471-2458
Abstract
Background
The appearance of the COVID-19 virus in December 2019, quickly escalated into a global crisis, prompting the World Health Organization to recommend regional lockdowns. While effective in curbing the virus’s spread, these measures have triggered intense debates on social media platforms, exposing widespread public anxiety and skepticism. The spread of fake news further fueled public unrest and negative emotions, potentially undermining the effectiveness of anti-COVID-19 policies. Exploring the narratives surrounding COVID-19 on social media immediately following the lockdown announcements presents an intriguing research avenue. The purpose of this study is to examine social media discourse to identify the topics discussed and, more importantly, to analyze differences in the focus and emotions expressed by the public in two countries (the UK and India). This is done with an analysis of a big corpus of tweets.
Methods
The datasets comprised of COVID-19-related tweets in English, published between March 29th and April 11th 2020 from residents in the UK and India. Methods employed in the analysis include identification of latent topics and themes, assessment of the popularity of tweets on topic distributions, examination of the overall sentiment, and investigation of sentiment in specific topics and themes.
Results
Safety measures, government responses and cooperative supports are common themes in the UK and India. Personal experiences and cooperations are top discussion for both countries. The impact on specific groups is given the least emphasis in the UK, whereas India places the least focus on discussions related to social media and news reports. Supports, discussion about the UK PM Boris Johnson and appreciation are strong topics among British popular tweets, whereas confirmed cases are discussed most among Indian popular tweets. Unpopular tweets in both countries pay the most attention to issues regarding lockdown. According to overall sentiment, positive attitudes are dominated in the UK whilst the sentiment is more neutral in India. Trust and anticipation are the most prevalent emotions in both countries. In particular, the British population felt positive about community support and volunteering, personal experiences, and government responses, while Indian people felt positive about cooperation, government responses, and coping strategies. Public health situations raise negative sentiment both in the UK and India.
Conclusions
The study emphasizes the role of cultural values in crisis communication and public health policy. Individualistic societies prioritize personal freedom, requiring a balance between individual liberty and public health measures. Collectivistic societies focus on community impact, suggesting policies that could utilize community networks for public health compliance. Social media shapes public discourse during pandemics, with popular and unpopular tweets reflecting and reshaping discussions. The presence of fake news may distort topics of high public interest, necessitating authenticity confirmation by official bloggers. Understanding public concerns and popular content on social media can help authorities tailor crisis communication to improve public engagement and health measure compliance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | COVID-19, Lockdown, Social media, Lexicon-based sentiment analysis, Structural topic modeling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Analytics, Technology & Ops Department |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Oct 2024 15:04 |
Last Modified: | 18 Oct 2024 16:14 |
Published Version: | https://bmcpublichealth.biomedcentral.com/articles... |
Status: | Published |
Publisher: | BMC |
Identification Number: | 10.1186/s12889-024-20160-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218098 |