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.

翻译：早期检测虚假新闻对于遏制其在社交媒体上的快速传播至关重要，这种传播会严重破坏公众信任和社会稳定。最新研究表明，与以往仅依赖内容的方法相比，融入传播动态能显著提升检测性能。然而，在早期阶段，由于缺乏可观测的传播信号，这仍然具有挑战性。为应对这一局限，我们提出AVOID，一种基于智能体驱动的虚拟传播方法，用于早期虚假新闻检测。AVOID将早期检测重新定义为一种新的证据生成范式，其中传播信号被主动模拟而非被动观测。通过利用具有差异化角色和数据驱动人格的LLM赋能智能体，AVOID能够在无需真实传播数据的情况下，逼真地构建早期扩散行为。由此产生的虚拟轨迹提供了互补的社会证据，丰富了基于内容的检测，同时一种去噪引导的融合策略将模拟传播与内容语义对齐。在基准数据集上的大量实验表明，AVOID始终优于最先进的基线方法，凸显了虚拟传播增强对于早期虚假新闻检测的有效性和实用价值。代码和数据可在https://github.com/Ironychen/AVOID获取。