In early 2020, the Corona Virus Disease 2019 (COVID-19) epidemic swept the world.In China, COVID-19 has caused severe consequences. Moreover, online rumors during  COVID-19 epidemic increased people's panic about public health and social stability. Understanding and curbing the spread of online rumor is an urgent task at present . Therefore, we analyzed the rumor spread mechanism and proposed a method to quantify the rumor influence by the speed of new insiders. We use the search frequency of rumor as an observation variable of new insiders. We calculated the peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution. Then we designed several rumor features and used the above two coefficients as predictable labels. The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient. Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient. Meanwhile, features which are conducive to the outbreak are usually harmful to the continued spread of rumors. At the same time, anxiety is a crucial rumor-causing factor. Finally, we discussed how to use deep learning technology to reduce  forecast loss by use BERT model.
In early 2020, the Corona Virus Disease 2019 (COVID-19) pandemic swept the world.In China, COVID-19 has caused severe consequences. Moreover, online rumors during the COVID-19 pandemic increased people's panic about public health and social stability. At present, understanding and curbing the spread of online rumors is an urgent task . Therefore, we analyzed the rumor spreading mechanism and propose a method to quantify a rumors' influence by the speed of new insiders. The search frequency of the rumor is used as an observation variable of new insiders. The peak coefficient and the attenuation coefficient are calculated for the search frequency, which conforms to the exponential distribution. We designed several rumor features and used the above two coefficients as predictable labels. A 5-fold cross-validation experiment using the mean square error (MSE) as the loss function showed that the decision tree was suitable for predicting the peak coefficient, and the linear regression model was ideal for predicting the attenuation coefficient. Our feature analysis showed that precursor features were the most important for the outbreak coefficient, while location information and rumor entity information were the most important for the attenuation coefficient. Meanwhile, features that were conducive to the outbreak were usually harmful to the continued spread of rumors. At the same time, anxiety was a crucial rumor causing factor. Finally, we discuss how to use deep learning technology to reduce the forecast loss by using the Bidirectional Encoder Representations from Transformers (BERT) model.
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