Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection

TL;DR

AVOID introduces an agent-driven virtual propagation method that simulates early-stage fake news diffusion, enhancing detection accuracy without relying on real propagation data, thus enabling more effective early fake news identification.

Contribution

This paper presents a novel paradigm of evidence generation through agent-driven virtual propagation, improving early fake news detection by simulating diffusion behaviors without real data.

Findings

AVOID outperforms state-of-the-art baselines on benchmark datasets.

Virtual propagation provides valuable social evidence for early detection.

The approach effectively simulates early diffusion behaviors without real propagation data.

Abstract

Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements show that incorporating propagation dynamics can significantly enhance detection performance compared to previous content-only approaches. However, this remains challenging at early stages due to the absence of observable propagation signals. To address this limitation, we propose AVOID, an \underline{a}gent-driven \underline{v}irtual pr\underline{o}pagat\underline{i}on for early fake news \underline{d}etection. AVOID reformulates early detection as a new paradigm of evidence generation, where propagation signals are actively simulated rather than passively observed. Leveraging LLM-powered agents with differentiated roles and data-driven personas, AVOID realistically constructs early-stage diffusion behaviors without requiring real propagation data. The resulting virtual trajectories provide complementary social evidence that enriches content-based detection, while a denoising-guided fusion strategy aligns simulated propagation with content semantics. Extensive experiments on benchmark datasets demonstrate that AVOID consistently outperforms state-of-the-art baselines, highlighting the effectiveness and practical value of virtual propagation augmentation for early fake news detection. The code and data are available at https://github.com/Ironychen/AVOID.