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

• URL: http://arxiv.org/abs/2601.02750v1
• Date: Tue, 06 Jan 2026 06:36:43 GMT
• Title: Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
• Abstract summary: We propose AVOID, an underlineagent-driven underlineopagatunderlineion for early fake news underlinedetection.<n>AVOID reformulates early detection as a new paradigm of evidence generation, where propagation signals are actively simulated rather than passively observed.
• Score: 30.547636224797376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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