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The outbreak of the infodemic during COVID-19 caused mass panic in society. Studying the evolution law of online rumors in the relatively complete cycle of COVID-19 can improve the pertinence of rumor governance. In this paper, the Python crawler program was used to collect rumor-refuting microblogs. Based on the development of public opinion and the pandemic, it was divided into five stages, including incubation stage, explosion stage, digestion stage, fluctuation stage, and re-digestion stage. Then, the statistical analysis methods ranging from correlation analysis to chi-square analysis and analytical methods like hot trend analysis, stage-based analyses of quantity, hot words and subjects were used to study the dynamic evolution laws of online rumors from the perspective of quantity and content. The research found that online rumors had the rules of periodic evolution of quantity and periodic fluctuation of hot words and subjects, which were in line with the COVID-19 pandemic. Social platforms such as WeChat and Tik Tok were the main places for rumors’ generation and propagation. In different stages of rumor transmission, the public mood was staggered repeatedly, and the homogeneous or same rumors would spread repeatedly. Based on the above laws, the corresponding governance strategies were put forward.

The evolution and governance of online rumors during the public health emergency: taking COVID-19 pandemic related rumors as an example