HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection

• URL: http://arxiv.org/abs/2607.28375v1
• Date: Thu, 30 Jul 2026 15:35:43 GMT
• Title: HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection
• Abstract summary: Video misinformation detection is often approached through global multimodal fusion or free-form multimodal reasoning.<n>We propose HyperClaim, a discriminative temporal hypergraph framework for sample-level authenticity classification.<n>Under the FactGuard temporal protocol, it achieves 83.7%, 82.0%, and 87.3% accuracy on FakeSV, FakeTT, and FakeVV.
• Score: 13.553737675973276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video misinformation detection is often approached through global multimodal fusion or free-form multimodal reasoning. Both paradigms can under-represent localized authenticity cues that arise from coupled interactions among query phrases, contextual text, and short temporal spans of frames. Because such interactions are inherently higher-order, pairwise graph formulations are insufficient to capture multi-way cross-modal dependencies, whereas hypergraphs offer a suitable representation for these relations. We propose HyperClaim, a discriminative temporal hypergraph framework for sample-level authenticity classification. Using the title or benchmark-provided paired text as a claim-like query, HyperClaim constructs a sparse heterogeneous hypergraph over query tokens, evidence tokens, and sampled frames; applies confidence-aware filtering and source budgeting to form compact text-frame and short-range temporal evidence units; performs adaptive soft-incidence reasoning with residual text-video calibration; and aggregates textual, visual, and hyperedge states through a discrepancy-aware readout. Without relying on generated rationales or external tool calls, HyperClaim preserves fine-grained cross-modal and temporal structure that global fusion tends to flatten. Under the FactGuard temporal protocol, it achieves 83.7%, 82.0%, and 87.3% accuracy on FakeSV, FakeTT, and FakeVV, respectively, outperforming strong discriminative and reasoning-centric baselines. Learned incidence and attention weights further reveal token- and frame-level structure.

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