  Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news
from real ones in extremely low-resource scenarios. This task has garnered
increased attention due to the widespread dissemination and harmful impact of
fake news on social media. Large Language Models (LLMs) have demonstrated
competitive performance with the help of their rich prior knowledge and
excellent in-context learning abilities. However, existing methods face
significant limitations, such as the Understanding Ambiguity and Information
Scarcity, which significantly undermine the potential of LLMs. To address these
shortcomings, we propose a Dual-perspective Augmented Fake News Detection
(DAFND) model, designed to enhance LLMs from both inside and outside
perspectives. Specifically, DAFND first identifies the keywords of each news
article through a Detection Module. Subsequently, DAFND creatively designs an
Investigation Module to retrieve inside and outside valuable information
concerning to the current news, followed by another Judge Module to derive its
respective two prediction results. Finally, a Determination Module further
integrates these two predictions and derives the final result. Extensive
experiments on two publicly available datasets show the efficacy of our
proposed method, particularly in low-resource settings.
