A Deep Learning Framework for News Diffusion Analysis on Reddit Using Graph Neural Networks

Temporal GNNs. Replacing static GraphSAGE with tem-poral graph networks (TGN) [20] or TGAT [19] would capture how influence evolves in real time, enabling early detection of emerging influencers within emerging threads. Cross-Platform Influence. Extending the framework to multi-layer networks spanning Reddit, Twitter, and Mastodon would provide a more complete picture of information diffu-sion in the modern media ecosystem. Bot and Coordinated Inauthentic Behavior Detection. Our model’s high-accuracy identification of structural anoma-lies in influence patterns could be extended to detect coordi-nated campaigns and bot networks, which exhibit characteris-tic deviations in betweenness and engagement patterns. Causal Influence Modeling. Replacing the correlational influence score with a causal framework—using counterfactual reasoning to estimate the change in cascade size caused by a user’s removal—would yield a more theoretically grounded influence measure. Federated and Privacy-Preserving Training. As Reddit’s API policies tighten, federated GNN training [32] that pre-serves user privacy while learning community-level models represents an important practical direction.

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