{"ID":2857625,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09464","arxiv_id":"2510.09464","title":"Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks","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.","short_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 predictabl...","url_abs":"https://arxiv.org/abs/2510.09464","url_pdf":"https://arxiv.org/pdf/2510.09464v2","authors":"[\"Patrick Gerard\",\"Luca Luceri\",\"Leonardo Blas\",\"Emilio Ferrara\"]","published":"2025-10-10T15:19:36Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Diffusion Model\"]","has_code":false}