JMIR Publications

ABSTRACT

Background:

Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence around the world.

Objective:

To assess and compare public responses towards containment measures during the initial outbreak and resurgence of COVID-19 epidemic in China.

Methods:

We collected all COVID-19 related posts from Sina Weibo (China’s Twitter) during the initial outbreak in China and resurgence in Beijing. With a Python script, we constructed subsets of Weibo posts focusing on three containment measures: lockdown, test-trace-isolate, and suspension of gatherings. Baidu’s open source sentiment analysis model, and Latent Dirichlet Allocation model, a widely-used machine learning algorithm, were used to assess public’s attention, sentiment, and frequently discussed topics on each containment measure.

Results:

During the initial outbreak, the amount of Weibo posts escalated dramatically following Wuhan lockdown, a high proportion of posts showed negative sentiment towards all three containment measures, and discrimination-related topics arose. Compared to the initial outbreak, the public expressed less attention and less negative sentiment on containment measures, and were more supportive towards containment measures during the COVID-19 resurgence. The public shifted their focus to the impacts of containment measures on their daily lives or work.

Conclusions:

As China’s response strategy switched from lockdown to a targeted response in the COVID-19 resurgence, public responses became more rational. When COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk population should be promoted to balance epidemic control and its impacts on daily lives and the economy.