An Extensive Search Trends-Based Analysis of Public Attention on Social Media in the Early Outbreak of COVID-19 in China.

Abstract

BackgroundA novel coronavirus (COVID-19) caused pneumonia broke out at the end of 2019 in Wuhan, China. Many cases were subsequently reported in other cities, which has aroused strong reverberations on the Internet and social media around the world.ObjectiveThe aim of this study was to investigate the reaction of global Internet users to the outbreak of COVID-19 by evaluating the possibility of using Internet monitoring as an instrument in handling communicable diseases and responding to public health emergencies.MethodsThe disease-related data were retrieved from China’s National Health Commission (CNHC) and World Health Organization (WHO) from January 10 to February 29, 2020. Daily Google Trends (GT) and daily Baidu Attention Index (BAI) for the keyword “Coronavirus” were collected from their official websites. Rumors which occurred in the course of this outbreak were mined from Chinese National Platform to Refute Rumors (CNPRR) and Tencent Platform to Refute Rumors (TPRR). Kendall’s Tau-B rank test was applied to check the bivariate correlation among the two indexes mentioned above, epidemic trends, and rumors.ResultsAfter the outbreak of COVID-19, both daily BAI and daily GT increased rapidly and remained at a high level, this process lasted about 10 days. When major events occurred, daily BAI, daily GT, and the number of rumors simultaneously reached new peaks. Our study indicates that these indexes and rumors are statistically related to disease-related indicators. Information symmetry was also found to help significantly eliminate the false news and to prevent rumors from spreading across social media through the epidemic outbreak.ConclusionCompared to traditional methods, Internet monitoring could be particularly efficient and economical in the prevention and control of epidemic and rumors by reflecting public attention and attitude, especially in the early period of an outbreak.