RAMM improves multimodal fake news detection by retrieving abstract narrative consistencies across instances and shifting to analogical reasoning via an MLLM backbone and two alignment modules.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Head-wise modality specialization via attention constraints and unimodal knowledge retention in MLLMs improves robustness to missing modalities in fake news detection while preserving full multimodal performance.
citing papers explorer
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Retrieval-Augmented Multimodal Model for Fake News Detection
RAMM improves multimodal fake news detection by retrieving abstract narrative consistencies across instances and shifting to analogical reasoning via an MLLM backbone and two alignment modules.
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Head-wise Modality Specialization within MLLMs for Robust Fake News Detection under Missing Modality
Head-wise modality specialization via attention constraints and unimodal knowledge retention in MLLMs improves robustness to missing modalities in fake news detection while preserving full multimodal performance.