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

2026-01-06

arXiv.org

AI Summary

Early fake news detection is challenging due to the scarcity of observable propagation signals. This work proposes AVOID, a novel approach that reframes the task as a social propagation evidence generation problem. By leveraging large language model–driven multi-agent simulations, AVOID proactively generates synthetic propagation trajectories for suspicious news, thereby producing informative propagation evidence to augment content-based analysis. The method integrates data-driven personality modeling, simulated virtual propagation, and a denoising-guided multimodal fusion strategy to effectively align content semantics with propagation dynamics. Extensive experiments demonstrate that AVOID significantly outperforms current state-of-the-art methods across multiple benchmark datasets, confirming the efficacy and practical value of virtual propagation augmentation for early-stage fake news detection.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Multiagent Systems: Adversarial Agents

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• fake news detection
• early detection
• propagation dynamics
• social media
• virtual propagation

Innovation

• agent-driven simulation
• virtual propagation
• early fake news detection
• LLM-powered agents
• evidence generation