Early and reliable short-video news verification through event-aware shot modeling and cross-modal evidence fusion

Whole-video aggregation and undifferentiated fusion can obscure when discrepancies arise and whether their sources are reliable. We present EASE-Fact, a framework for content-based news screening that models ordered visual units under an explicit observation boundary. A causal event-evolution transformer captures title-conditioned transitions, while a reliability-aware router models typed cross-modal relations and distinguishes inconsistent evidence from absent or unreliable sources.

The Core model supports complete-video classification and a separately trained streaming configuration with prefix-local preprocessing and committed transcripts. The Full model adds temporally admissible historical debunking records and preserves the Core prediction when usable evidence is unavailable. Chronological and event-disjoint evaluations, matched comparisons, online first-crossing alerts, component ablations, and manual localization assess the framework’s performance and practical boundaries.

The findings support temporal structure and source-quality modelling, while statistical uncertainty and event-exclusion analyses qualify comparative and retrieval gains. EASE-Fact provides inspectable diagnostics for news communication and editorial prioritization; benchmark predictions and internal conflicts remain distinct from independent determinations of factual truth. This research received no external funding.

School of Journalism and Communication, Pingdingshan University, Pingdingshan, 467000, Henan, China The author declares no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Below is the link to the electronic supplementary material.

Dataset partitions, localization results, retrieval-threshold analysis, and computational resource details are provided in Tables S1–S4. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.

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