Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom

TL;DR

This paper introduces a novel explainable fake news detection framework that leverages large language models and a defense mechanism to improve accuracy and provide justifications, addressing biases in crowd wisdom.

Contribution

The paper proposes a defense-based framework with evidence extraction, prompt-based justification generation, and veracity inference, advancing explainable fake news detection methods.

Findings

Outperforms state-of-the-art baselines in detection accuracy

Provides high-quality, concise justifications

Effective in handling diverse and competing narratives

Abstract

Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since the wisdom of crowds is uncensored. To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences. To gain concise insights from evidences, we then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities. Finally, we propose a defense-based inference module to determine veracity via modeling the defense among these justifications. Extensive experiments conducted on two real-world benchmarks demonstrate that our proposed method outperforms state-of-the-art baselines in terms of fake news detection and provides high-quality justifications.