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Exploring Spillover Effects for COVID-19 Cascade Prediction.

Ninghan Chen1, Xihui Chen2, Zhiqiang Zhong1

  • 1Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg.

Entropy (Basel, Switzerland)
|February 25, 2022
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Summary

This study introduces the spillover effect to predict online information popularity during the COVID-19 pandemic. Incorporating this effect significantly enhances cascade prediction for public health messages.

Keywords:
COVID-19Twittercascade predictiongraph neural networksinformation diffusionspillover effects

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Area of Science:

  • Social Media Analysis
  • Information Science
  • Computational Social Science

Background:

  • The COVID-19 pandemic fueled an infodemic, characterized by information outbreaks on social media.
  • Predicting online content popularity (cascade prediction) is crucial for managing information, including identifying and mitigating the spread of misinformation.
  • Existing cascade prediction models have not explored the 'spillover effect'—how exposure to information influences users' decisions to diffuse it.

Purpose of the Study:

  • To investigate the existence and impact of the spillover effect on information diffusion related to COVID-19 preventive measures.
  • To enhance existing cascade prediction methods by incorporating the identified spillover effect.
  • To improve the accuracy of predicting the popularity of online content, particularly health-related information.

Main Methods:

  • Collected a Twitter dataset focused on COVID-19 preventive measures.
  • Validated the presence of spillover effects within the collected data.
  • Extended three Graph Neural Network (GNN)-based cascade prediction models to include spillover effect mechanisms.

Main Results:

  • Confirmed the existence of spillover effects in the diffusion of COVID-19 information on Twitter.
  • Demonstrated that incorporating spillover effects significantly improves the performance of GNN-based cascade prediction models.
  • Showcased enhanced prediction accuracy for both COVID-19 preventive measures and other related messages.

Conclusions:

  • The spillover effect is a significant factor in online information diffusion, particularly during health crises.
  • Integrating spillover effects into GNN models offers a substantial advancement in cascade prediction accuracy.
  • This approach aids in proactively identifying and managing impactful online content, including public health information and misinformation.