  An information outbreak occurs on social media along with the COVID-19
pandemic and leads to infodemic. Predicting the popularity of online content,
known as cascade prediction, allows for not only catching in advance hot
information that deserves attention, but also identifying false information
that will widely spread and require quick response to mitigate its impact.
Among the various information diffusion patterns leveraged in previous works,
the spillover effect of the information exposed to users on their decision to
participate in diffusing certain information is still not studied. In this
paper, we focus on the diffusion of information related to COVID-19 preventive
measures. Through our collected Twitter dataset, we validated the existence of
this spillover effect. Building on the finding, we proposed extensions to three
cascade prediction methods based on Graph Neural Networks (GNNs). Experiments
conducted on our dataset demonstrated that the use of the identified spillover
effect significantly improves the state-of-the-art GNNs methods in predicting
the popularity of not only preventive measure messages, but also other COVID-19
related messages.
