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Tracking evolving communities in fake news cascades using temporal graphs.

Yanfei Ma1, Daozheng Qu2, Yibo Wang3

  • 1Department of Computer Science, Fairleigh Dickinson University, Vancouver, V6B 2P6, Canada.

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We developed TIDE-MARK to track fake news communities on social media over time. This method effectively identifies stable, interconnected communities spreading misinformation, outperforming existing techniques.

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Dynamic community detectionFake news cascadesReinforcement learningSocial media fake newsTemporal graph networks

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • Misinformation spreads rapidly on social media platforms with dynamic user populations and evolving community structures.
  • Existing methods often overlook temporal dynamics or use static clustering, failing to capture evolving community behavior in information cascades.
  • Longitudinal tracking of communities within information cascades is complicated by their continuous development, amalgamation, or disintegration.

Purpose of the Study:

  • To propose TIDE-MARK, a novel methodology for identifying communities within fake news cascades that maintain structural and temporal consistency.
  • To provide a unified framework for consistent and interpretable community trajectories in dynamic social networks.
  • To evaluate the effectiveness of TIDE-MARK against robust baselines using real-world fake news datasets.

Main Methods:

  • Employing node embeddings via temporal graph neural networks to represent network structure and dynamics.
  • Utilizing prototype-driven clustering and Markov modeling for community detection and transition analysis.
  • Implementing reinforcement-based refinement for enhanced accuracy and stability of community identification.

Main Results:

  • TIDE-MARK demonstrated superior performance over baselines in both structural (modularity, conductance) and temporal (adjusted Rand index) measures.
  • Analysis revealed that fake news propagates through more stable, interconnected communities, unlike real news which spreads via scattered communities.
  • Simulations indicated that structure-aware interventions targeting nascent communities can significantly reduce misinformation spread and cascade modularity.

Conclusions:

  • TIDE-MARK provides a robust, structure-aware framework for real-time fake news monitoring, prioritizing network dynamics over content analysis.
  • The methodology offers a foundation for innovative dynamic community monitoring in complex social systems.
  • The interpretable architecture of TIDE-MARK supports ethical applications and the development of content-neutral mitigation strategies against misinformation.