Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data

This study examines the dynamic relationship between China's COVID-19 containment policies and public sentiment, focusing on the significant lockdowns in Wuhan and Shanghai. We employed natural language processing (NLP) on Weibo text data to uncover how people's emotions towards these containment measures changed over time and space. Our analysis reveals a critical evolution in public sentiment, transitioning from initial support to growing dissatisfaction, highlighting the impact of 'pandemic fatigue' and the socio-economic factors influencing these shifts. This study contributes to understanding the complex interplay between public health strategies and societal reactions, providing practical insights into the spatial variations of sentiment across different demographic and socio-economic groups. By elucidating the causal effects of containment policies on public sentiment and the subsequent rise in public skepticism, our research offers valuable lessons for policymakers in tailoring communication and interventions to mitigate negative public perceptions and foster compliance during health crises.

Zhihang Liu, Jinlin Wu, Connor Y. H. Wu, Xinming Xia