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

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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.