Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
Ye Liu, Jiajun Zhu, Xukai Liu, Haoyu Tang, Yanghai Zhang, Kai Zhang, Xiaofang Zhou, Enhong Chen

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…
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Taxonomy
TopicsMisinformation and Its Impacts · Spam and Phishing Detection · Advanced Malware Detection Techniques
MethodsSoftmax · Attention Is All You Need
