  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.
