  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.
