This study examines the dynamic relationship between China's COVID-19 containment policies and public sentiment, focusing on the lockdowns in Wuhan and Shanghai. Using natural language processing (NLP) on Weibo data, the study reveals a shift in public sentiment from initial support to growing dissatisfaction, highlighting the impact of 'pandemic fatigue' and socio-economic factors. The research offers insights into the spatial variations of sentiment across different demographic and socio-economic groups, providing valuable lessons for policymakers in mitigating negative public perceptions and fostering compliance during health crises.

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