Centre for Digital Public Health in Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom.
Department of Computer Science, University College London, London, United Kingdom.
Front Public Health. 2024 Feb 21;12:1105383. doi: 10.3389/fpubh.2024.1105383. eCollection 2024.
To 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.
In 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.
The 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.
While 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.
在 COVID-19 大流行期间，为了保护公民安全，英国乃至全球的日常生活都前所未有地受到了公共卫生限制。在 COVID-19 等紧急情况下，政策制定者能够近乎实时地衡量公众对这些措施的反应和情绪，并为社交媒体在应急响应中的使用建立最佳实践，这一点至关重要。
在这项研究中，我们探索了 Twitter 作为评估公众对大流行反应的数据源。我们通过从 2020 年 5 月 26 日至 2021 年 3 月 8 日收集的 2500 万条英国推文，使用主题情感分析方法进行了分析。我们通过递归神经网络的情感分析和嵌入式主题模型的主题聚类相结合，实现了创新的组合。
结果表明，在英国，通过时间和地理位置可以解释每个主题的情感信号，这些信号可以与大流行期间的特定公共卫生和政策事件联系起来。本研究的独特之处在于，将推断出的情绪趋势与英国国家统计局进行的行为调查进行对比，为政策发布时的公众情绪提供了一个强有力的衡量标准。
虽然现有研究大多集中在特定问题或新技术上，但我们为政策制定者评估公众对 COVID-19 的反应开发了一个全面的框架，并且该框架可推广到未来的紧急情况。新兴方法不仅阐明了公众对 COVID-19 政策的立场，还为公众政策制定者建立了一个可实时监测和评估其政策的可推广框架。此外，所提出的方法可以作为政策制定者的工具进行推广，并且可以应用于其他政治和公共利益主题。
