Understanding Public Emotions: Spatiotemporal Dynamics in the Post-Pandemic Era Through Weibo Data.

Abstract

Prolonged exposure to public health crises in the post-pandemic era poses significant threats to global mental health. To address this, we developed a conceptual model to analyse the spatiotemporal distribution of public emotions, using Weibo data from the 2022 Beijing bar outbreak (9 June-18 August). The model integrates lexicon-based emotion analysis, spatial autocorrelation tests, and content analysis to provide a comprehensive understanding of emotional responses across stages and regions. The findings reveal a multi-peak emotional cycle spanning emergency, contagion, and resolution stages, with significant emotional clustering in emergency zones, surrounding areas, and regions visited by infected individuals. Through coding, we identified 24 main-categories and 90 sub-categories, distilled into nine core themes that illustrate the interplay between influencing factors, public emotions, and online behaviours. Positive public emotions (e.g., hopefulness, gratitude, optimism) were linked to pandemic improvements and policy implementation, driving behaviours such as supporting prevention measures and resisting misinformation. Negative emotions (e.g., anger, anxiety, sadness) stemmed from severe outbreaks, insufficient controls, and restrictions on freedoms, leading to criticism and calls for accountability. This study bridges big data analytics with behavioural science, offering critical insights into evolving public emotions and behaviours. By highlighting spatiotemporal patterns and emotional dynamics, it provides actionable guidance for governments and health organizations to design targeted interventions, foster resilience, and better manage future social crises with precision and empathy.