Author(s)

Zhaobo Zeng, Kai Tang, Ke Xing, Yingyin Wu, Haisong Xiong

Corresponding Author

Zhaobo Zeng

ABSTRACT

Based on Selenium data mining technology, the epidemiological characteristics of real help-seeking cases in Sina Weibo were obtained through the analysis of 690 effective cases in Sina Weibo "pneumonia patients seeking help" from February 4 to 22, 2020. The study found that 97.6% of the patients seeking help came from Wuhan, focusing on central urban areas such as Wuchang, Dengkou and Hanyang, which was proportional to local medical resources and population density. The cases of Weibo seeking help were mainly distributed from February 4 to 7. With the relief of the shortage of medical resources, the number of cases seeking help through Weibo decreased significantly. The diagnosis date of the patients seeking help was mainly distributed from January 16 to February 6, which was basically consistent with the case distribution released by the Chinese Centers for Disease Control and Prevention. The median age distribution of patients seeking help is 60 years old, which is significantly higher than the data released by the Chinese Centers for Disease Control and Prevention, but it is basically consistent with the data of Wuhan Central Hospital. The results show that for major sudden infectious diseases, social media such as Weibo not only play a role in the spread of public opinion, but also play an important role in epidemiological analysis. Based on the real-time and extensiveness of social media, combined with data mining and big data analysis, it is helpful for decision-makers to quickly grasp the real situation.

KEYWORDS

COVID-19; data Mining; Epidemiology; Social Media