发表机构
AI and Social Good Lab, AI Media Centre; Southern University of Science and Technology; Beijing Normal University; Hong Kong Baptist University（AI媒体中心人工智能与社会公益实验室; 南方科技大学; 北京师范大学; 香港浸会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对假新闻短视频检测与解释任务，提出NVKE-CEI统一系统，结合关键帧提取与双LLM事实核查器融合，实验证明优于现有方法。
AI 中文摘要
短视频平台已成为公众获取新闻的主要来源，这也使得假新闻视频得以广泛传播。我们研究了假新闻视频检测与解释（FNVDE）任务。现有方法面临两个关键局限。首先，常用的帧选择策略可能遗漏与真实性相关的线索，或为理解新闻视频提供不足的时间上下文。其次，先前的方法忽视了多模态理解或证据检索。为解决这些局限，我们提出了NVKE-CEI，一个统一系统，集成了新闻视频关键帧提取方法（NVKE）和利用内容与证据信息（CEI）的FNVDE框架。NVKE基于视觉和OCR文本相似度组合的时间变化来选择关键帧。CEI采用两个专门的基于LLM的事实核查器（基于内容的和基于证据的），其输出由轻量级评判模型融合。大量实验表明，NVKE-CEI在生成高质量内容依据解释的同时，优于最先进的基线方法。
英文摘要
Short-video platforms have become a primary news source for the public, which has also enabled the widespread dissemination of fake news videos. We study the task of fake news video detection and explanation (FNVDE). Existing methods face two critical limitations. First, commonly used frame selection strategies may omit veracity-relevant cues or provide insufficient temporal context for understanding news videos. Second, prior methods neglect either multimodal understanding or evidence retrieval. To address these limitations, we propose NVKE-CEI, a unified system that integrates a news video keyframes extraction method (NVKE) and an FNVDE framework leveraging both content and evidence information (CEI). NVKE selects keyframes based on chronological changes in combined visual and OCR-text similarity. CEI employs two specialized LLM-based fact checkers (content-based and evidence-based) whose outputs are fused by a lightweight judge model. Extensive experiments show that NVKE-CEI outperforms state-of-the-art baselines while generating high-quality content-grounded explanations.
CommentsAccepted to the Findings of EMNLP 2026
