Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection

TL;DR

This paper introduces DKFND, a knowledge-guided framework that enhances few-shot fake news detection by integrating inside and outside information retrieval, significantly improving performance in low-resource scenarios.

Contribution

The paper proposes a novel Dual-perspective Knowledge-guided Fake News Detection (DKFND) model that combines knowledge concept detection, information retrieval, relevance evaluation, and final decision-making.

Findings

DKFND outperforms existing methods on two public datasets.

The framework is especially effective in low-resource settings.

Extensive experiments validate the model's robustness and accuracy.

Abstract

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 Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DKFND first identifies the knowledge concepts of each news article through a Detection Module. Subsequently, DKFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to evaluate the relevance and confidence of them. Finally, a Determination Module further derives two respective predictions and obtain the final result. Extensive experiments on two public datasets show the efficacy of our proposed method, particularly in low-resource settings.