News media, particularly video-based platforms, have become deeply embedded in daily life, concurrently amplifying the risks of misinformation dissemination. Consequently, multimodal fake news detection has garnered significant research attention. However, existing datasets predominantly comprise user-generated videos characterized by crude editing and limited public engagement, whereas professionally crafted fake news videos disseminated by media outlets—often politically or virally motivated—pose substantially greater societal harm. To address this gap, we construct FMNV, a novel dataset exclusively composed of news videos published by media organizations. Through empirical analysis of existing datasets and our curated collection, we categorize fake news videos into four distinct types. Building upon this taxonomy, we employ Large Language Models (LLMs) to automatically generate deceptive content by manipulating authentic media-published news videos. Furthermore, we propose FMNVD, a baseline model featuring a dual-stream architecture that integrates spatio-temporal motion features from a 3D ResNeXt-101 backbone and static visual semantics from CLIP. The two streams are fused via an attention-based mechanism, while co-attention modules refine the visual, textual, and audio features for effective multimodal aggregation. Comparative experiments demonstrate both the generalization capability of FMNV across multiple baselines and the superior detection efficacy of FMNVD. This work establishes critical benchmarks for detecting high-impact fake news in media ecosystems while advancing methodologies for cross-modal inconsistency analysis.