From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects

TL;DR

This paper investigates the spillover effect in COVID-19 related information diffusion on social media, demonstrating its significance and enhancing cascade prediction models with Graph Neural Networks for better accuracy.

Contribution

It identifies and validates the spillover effect in COVID-19 information spread and extends GNN-based cascade prediction methods to incorporate this effect, improving prediction performance.

Findings

Spillover effect significantly improves cascade prediction accuracy.

Enhanced GNN models outperform state-of-the-art methods.

The effect applies to various COVID-19 related messages.

Abstract

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.