Application of the Online Big Data Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19 (Preprint)

Abstract

BACKGROUND The outbreak of the COVID-19 epidemic in 2019 exerted an enormous global public reaction. OBJECTIVE The online big data reflects public attention of hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm the primitive correlation between COVID-19 related data and Chinese online data. METHODS Bivariate correlation statistics was used to check the relationship between epidemic trends of the BDI and SMI, and identify the difference of public concerns about COVID-19 between the epidemic area (Hubei province) and non-epidemic area (all other provinces). RESULTS The public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak (Pearson correlation coefficient =0.807, P<0.001). But compared with the SMI, the BDI was more closely related to the actual epidemic. The BDI and SMI had correlations with new confirmed cases (P<0.01), cumulative confirmed cases (P<0.01), cumulative death cases (P<0.01), new cured discharged cases (P<0.01), and cumulative cured discharged cases (P<0.01), but not with new death cases. Besides, the public's demand for information on COVID-19 was consistent and urgent across the country (Spearman correlation coefficient=0.930, P<0.001), regardless of the location of the epidemic area. CONCLUSIONS The public paid more attention to indicators of confirmed cases due to numerous irresistible factors and cured circumstances with positive outcomes. But the public had a lag in the attention of COVID-19 in the non-epidemic area. In the risk communication of public health emergencies, relevant departments can effectively use the information dissemination characteristics of the Baidu search engine and Sina Weibo, to convey front-line information to the public timely and accurately, and improve the effectiveness of risk communication.