TIDE-MARK: A Temporal Graph Framework forTracking Evolving Communities in Fake News Cascades

Misinformation proliferates on social media platforms owing to both static and dynamic user populations. The development,amalgamation, or disintegration of communities throughout an information cascade complicates the longitudinal tracking of thesecommunities. Numerous contemporary methodologies either neglect temporal factors or employ static clustering techniques,which do not accommodate dynamic coordination. We propose TIDE-MARK, a methodology designed to identify communitiesinside fake news cascades that exhibit consistency in both structure and temporal dynamics. The methodology encompassesnode embeddings via temporal graph neural networks, prototype-driven clustering, Markov modeling of community transitions,and reinforcement-based refinement. The unified design facilitates consistent and comprehensible community trajectories.Three empirical datasets pertaining to political, entertainment, and health-related fake news are utilized to evaluate TIDE-MARK. The databases include PolitiFact, GossipCop, and ReCOVery. Our model surpasses robust baselines regardingstructural (modularity, conductance) and temporal (adjusted Rand index) measures, supported by consistent effect sizes.Structural research indicates that real news spreads through more scattered and less organized communities, while falsenews propagates through more stable and well interconnected communities. Our objective is to assess the viability ofinterventions by simulating a structure-aware approach that targets important users in nascent communities. The substantialreduction in cascade modularity and spread demonstrated in the results evidences the efficacy of content-neutral mitigationtechniques. TIDE-MARK offers a transparent and privacy-preserving approach for real-time fake news monitoring, emphasizingstructure-aware strategy signals over textual analysis. It establishes a foundation for innovative methods of dynamic communitymonitoring inside complex social systems and features an interpretable architecture that enables ethical application.