Adopting Topic Modelling Approaches to Analyse Post-Pandemic Changes in Public Risk Perception

采用主题建模方法分析疫情后公众风险认知的变化

• 批准号: 2588225
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2588225
• 关键词: Adopting; Topic; Modelling; Approaches; Analyse

The COVID-19 pandemic has caused unprecedented changes to social life. Government efforts to minimise the spread of the disease have emphasised behavioural interventions, including encouraging protective measures such as social distancing. Public engagement in protective measures may be linked to individuals' perceived risk of contracting the virus. While most of the literature studying risk perception during the pandemic has focused on cross-sectional analysis, little attention has been paid to identifying longitudinal social changes. This research proposes to develop a holistic framework for analysing post-pandemic changes in public risk perception which may engender widespread social change. It aims to leverage unsupervised machine learning methodologies, such as topic modelling, to analyse unstructured social media datasets related to the pandemic. We believe this project will enrich previous research focused on a computational analysis of public risk perception.

199年大流行对社会生活造成了前所未有的变化。政府为最大程度地减少疾病传播的努力强调了行为干预，包括鼓励采取保护性措施，例如社会疏远。公众参与保护措施可能与个人感知的病毒感知风险有关。尽管大流行期间研究风险感知的大多数文献都集中在横截面分析上，但很少关注确定纵向社会变化。这项研究建议开发一个整体框架，用于分析公共风险感知大流行后的变化，这可能会导致广泛的社会变革。它旨在利用无监督的机器学习方法（例如主题建模）来分析与大流行有关的非结构化社交媒体数据集。我们认为，该项目将丰富以前的研究，重点是对公共风险感知的计算分析。