FactGuard: Event-Centric and Commonsense-Guided Fake News Detection

TL;DR

FactGuard leverages large language models to focus on event-centric content and factual reasoning, improving fake news detection robustness and accuracy while addressing style mimicry and usability issues.

Contribution

Introduces a novel LLM-based fake news detection framework with event-centric content extraction, dynamic contradiction detection, and knowledge distillation for efficiency.

Findings

Outperforms existing methods in accuracy and robustness

Effectively reduces style sensitivity in fake news detection

Operates efficiently in resource-constrained scenarios

Abstract

Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative potential, LLMs remain an untapped goldmine for fake news detection, with their real-world adoption hampered by shallow functionality exploration, ambiguous usability, and prohibitive inference costs. In this paper, we propose a novel fake news detection framework, dubbed FactGuard, that leverages LLMs to extract event-centric content, thereby reducing the impact of writing style on detection performance. Furthermore, our approach introduces a dynamic usability mechanism that identifies contradictions and ambiguous cases in factual reasoning, adaptively incorporating LLM advice to improve decision reliability. To ensure efficiency and practical deployment, we employ knowledge distillation to derive FactGuard-D, enabling the framework to operate effectively in cold-start and resource-constrained scenarios. Comprehensive experiments on two benchmark datasets demonstrate that our approach consistently outperforms existing methods in both robustness and accuracy, effectively addressing the challenges of style sensitivity and LLM usability in fake news detection.