Systematic evaluation of COVID-19 related Internet health rumors during the breaking out period of COVID-19 in China

Abstract

Background: To adapt the scientific evaluation tool for the confusion evaluation of health rumors and to test this tool to the confusion evaluation of coronavirus disease 2019 (COVID- 19)-related health rumors on Chinese online platforms during the outbreak period of COVID-19 in China. Methods: The design of our study was systematic evaluation of COVID-19-related health rumors. Retrieved from 7 rumor-repellent platforms, rumors about COVID-19 were collected during the publication from December 1, 2019 to February 6, 2020, and their origins were traced. Researchers evaluated rumors using the confusion evaluation tool in 6 dimensions (creators, evidence selection, evidence evaluation, evidence application, backing and publication platform, conflict of interest). Items were scored using a seven-point Likert scale. The scores were converted into percentages, and the median of rumors from different sources was compared with rank sum test. Results: Our research included 127 rumors. Scores were converted to percentages, median and interquartile range are used to describe the data. The median score: creators 25.00% (interquartile range, IQR, 16.67-37.50%), evidence selection 27.78% (IQR, 13.89-44.44%), evidence evaluation 33.33% (IQR, 25.00-45.83%), evidence application 36.11% (IQR, 22.22- 47.22%), backing and publication platform 8.33% (IQR, 4.17-20.83%), conflict of interest 75.00% (IQR, 50.00-83.33%). Almost 40% rumors came from WeChat and the rumors with the lowest scores were concentrated on the WeChat platform. The rumors about prevention methods have relatively lower scores. Conclusion: Most rumors included were not highly confusing for evaluators of this project. We Chat is the "worst-hit area"of COVID-19 related health rumors. More than half rumors focus on the description of prevention methods, which reflects the panic, anxiety and blind conformity of the public under public health emergencies.