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arXiv · 2510.09464

Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks

Patrick Gerard·Luca Luceri·Leonardo Blas·Emilio Ferrara
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Abstract

Online narratives spread unevenly across platforms, with content emerging on one site often appearing on others, hours, days or weeks later. Existing cross-platform information diffusion models often treat platforms as isolated systems, disregarding cross-platform activity that might make these patterns more predictable. In this work, we frame cross-platform prediction as a network proximity problem: rather than tracking individual users across platforms or relying on brittle signals like shared URLs or hashtags, we construct platform-invariant discourse networks that link users through shared narrative engagement. We show that cross-platform neighbor proximity provides a strong predictive signal: adoption patterns follow discourse network structure even without direct cross-platform influence. Our highly-scalable approach substantially outperforms diffusion models and other baselines while requiring less than 3% of active users to make predictions. We also validate our framework through retrospective deployment. We sequentially process a datastream of 5.7M social media posts occurred during the 2024 U.S. election, to simulate real-time collection from four platforms (X, TikTok, Truth Social, and Telegram): our framework successfully identified emerging narratives, including crises-related rumors, yielding over 94% AUC with sufficient lead time to support proactive intervention.

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Patrick Gerard, Luca Luceri, Leonardo Blas, Emilio Ferrara. 2025-10-10. Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks. https://arxiv.org/abs/2510.09464

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