Spread Mechanism and Influence Measurement of Online Rumors in China During the COVID-19 Pandemic
This research provides insights into the spread of online rumors during a public health crisis, which is important for authorities and social media platforms to better manage misinformation and reduce public panic.
This paper analyzed the spread of online rumors in China during the COVID-19 pandemic and proposed a method to quantify rumor influence by the speed of new insiders, using search frequency as an observation variable. They found that decision trees were suitable for predicting the peak coefficient of rumor spread, while linear regression models were ideal for predicting the attenuation coefficient.
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.