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Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection

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arxiv 2407.19493 v3 pith:LTYMPDK3 submitted 2024-07-28 cs.CV cs.AIcs.MM

Yihao Wang , Lizhi Chen , Zhong Qian , Peifeng Li This is my paper · ORCID
classification cs.CVcs.AIcs.MM
keywords newsdatasetmultimodaldetectionfakemodelvideosattention
verification ladder T0 review T1 audit T2 compute T3 formal
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News media, especially video news media, have penetrated into every aspect of daily life, which also brings the risk of fake news. Therefore, multimodal fake news detection has recently garnered increased attention. However, the existing datasets are comprised of user-uploaded videos and contain an excess amounts of superfluous data, which introduces noise into the model training process. To address this issue, we construct a dataset named Official-NV, comprising officially published news videos. The crawl officially published videos are augmented through the use of LLMs-based generation and manual verification, thereby expanding the dataset. We also propose a new baseline model called OFNVD, which captures key information from multimodal features through a GLU attention mechanism and performs feature enhancement and modal aggregation via a cross-modal Transformer. Benchmarking the dataset and baselines demonstrates the effectiveness of our model in multimodal news detection.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation

    cs.CL 2026-01 unverdicted novelty 7.0 of 10

    Multimodal LLMs exhibit different levels of susceptibility to misinformation in short videos, with Gemini-2.5-Pro showing the highest resistance (belief score 71.5) and o3 the lowest (35.2).

  2. A New Dataset and Benchmark for Grounding Multimodal Misinformation

    cs.SI 2025-09 conditional novelty 6.0 of 10

    GroundLie360 is a 2,044-video Snopes-derived benchmark with fine-grained annotations localizing six types of multimodal misinformation; the VLM prompting baseline FakeMark shows the task remains difficult.

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