As the pandemic of social media panic spreads faster than the COVID-19 outbreak, an urgent challenge arises: a prediction model needs to be developed to predict the future diffusion size of a piece of COVID-19 information at an early stage of its emergence. In this paper, we focus on the cascade prediction of COVID-19 information with spillover effects. We build the first COVID-19-related Twitter dataset of the Greater Region from the cascade perspective and explore the structure of the cascades. Moreover, the existence of spillover effects is verified in our data and spillover effects for information on COVID-19 symptoms, anti-contagion and treatment measures are found to be from multiple topics of other information. Building on the above findings, we design our SE-CGNN model (CoupledGNN with spillover effects) based on CoupledGNN for cascade prediction. Experiments conducted on our dataset demonstrate that our model outperforms the state-of-the-art methods for COVID-19 information cascade prediction.

翻译：由于社交媒体恐慌的蔓延速度比COVID-19爆发的速度快,因此出现了一项紧迫的挑战:需要开发一种预测模型,以预测COVID-19信息在出现初期的未来扩散规模;在本文件中,我们侧重于对COVID-19信息的连锁预测并产生外溢效应;我们从级联的角度建立与COVID-19有关的大区第一个Twitter数据集,并探索级联的结构;此外,在我们的数据和外溢效应方面,对COVID-19症状、抗扰和治疗措施的信息的外溢效应的存在进行了核实,发现这些效应来自其他信息的多个专题。我们根据上述调查结果设计了SE-CGNN模型(具有外溢效应的CUplateGNNN),以PupedGNNN(PUPUDGNNNN)为基础,用于级预测。在我们的数据集上进行的实验表明,我们的模型比COVID-19信息级联算出最新方法。