The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://github.com/ICTMCG/NEED.

翻译：短视频平台的普及催生了大量假新闻视频，这些视频相比文本类假新闻具有更强的传播能力。因此，自动检测假新闻视频已成为实践中的重要应对手段。现有研究通常仅基于多模态信息对每个新闻视频进行独立验证。然而，针对同一事件的多视角新闻视频往往被共同发布，彼此间包含互补或矛盾的信息，因而可用于相互评估。为此，我们提出了一种新颖且实用的范式，即跨样本假新闻视频检测，并设计了一个名为NEED（邻域增强假新闻视频检测）的新型框架，该框架整合了属于同一事件的新闻视频间的邻域关系。NEED可便捷地与现有单样本检测器结合，并通过所提出的图聚合（GA）模块和辟谣纠正（DR）模块进一步提升其性能。具体而言，在获得单样本检测器生成的特征表示后，GA利用动态图聚合邻域信息以丰富独立样本的特征；随后，DR通过文本与视觉一致性，显式利用辟谣视频与假新闻视频之间的关系对候选视频进行驳斥。在公开基准上的大量实验表明，NEED显著提升了单模态（准确率最高提升8.34%）和多模态（准确率最高提升4.97%）基础检测器的性能。代码已开源至 https://github.com/ICTMCG/NEED。