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.

COVID-19大流行给社会生活带来了前所未有的变化。政府为尽量减少疾病传播所做的努力强调了行为干预，包括鼓励保持社交距离等保护措施。公众对保护措施的参与可能与个人感知的感染病毒的风险有关。虽然大多数研究大流行期间风险认知的文献都集中于横断面分析，但很少关注识别纵向社会变化。这项研究建议开发一个整体框架来分析大流行后公众风险认知的变化，这可能会引发广泛的社会变革。它的目的是利用无监督的机器学习方法（例如主题建模）来分析与大流行相关的非结构化社交媒体数据集。我们相信该项目将丰富之前专注于公共风险感知计算分析的研究。