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疫情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，包括鼓励采取保护措施，如社交距离。公众对保护措施的参与可能与个人对感染病毒风险的感知有关。虽然大部分研究疫情期间风险认知的文献都侧重于横截面分析，但很少关注识别纵向社会变化。这项研究建议建立一个整体框架，分析大流行后公众风险认知的变化，这些变化可能导致广泛的社会变革。它旨在利用主题建模等无监督机器学习方法，分析与大流行病有关的非结构化社交媒体数据集。我们相信这个项目将丰富以前的研究集中在公共风险感知的计算分析。