Abstract
An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose 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 effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.
Highlights
The outbreak of the COVID-19 pandemic leads to an infodemic, which is partially attributed to the outbreak of information on major online social networks (OSNs), includingTwitter, Facebook, Instagram, and YouTube [1]
We will leverage deep learning to automatically capture the contents of tweets exposed to users that impose strong info-exposure spillover effects, and improve the accuracy of cascade prediction
We use the framework of Graph Neural Networks (GNN) to learn the magnitudes of the info-exposure spillover effect of a user’s exposed information on his/her behaviour of retweeting preventive measure messages
Summary
The outbreak of the COVID-19 pandemic leads to an infodemic, which is partially attributed to the outbreak of information on major online social networks (OSNs), including. We adopt the original definition of a behaviour spillover effect which intuitively means “the observable and causal effect that a change in one behaviour has on a different, subsequent behaviour” [11] According to this definition, the info-exposure spillover effect studied in this paper can be interpreted as the impact of the information a user perceived from the social media on his/her behaviour of forwarding a COVID-19 related post received from his/her friends. All messages present certain a level of spillover effects on retweeting preventive messages, those related to COVID-19 have stronger impacts This motivates us to extend existing state-of-the-art cascade prediction models by taking into account info-exposure spillover effects. The results show an obvious increase in accuracy due to the use of the info-exposure spillover effect
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