通过图分析追踪俄乌电报频道间谣言叙事传播
Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
- 构建传播图，聚合语义相关言论为叙事簇
- 发现协同传播模式，揭示谣言跨频道扩散路径
- 适合关注社交媒体信息战与网络舆论分析者
在社交媒体上检测虚假信息叙事面临规模大、演化快、语言多变等挑战。本文提出一种基于图的框架，结合弱监督与传播图分析，识别并分析电报（Telegram）生态系统中的虚假信息叙事。该方法将语义相关的陈述聚类为叙事层级，并建模其在相互连接的频道间的扩散过程，从而捕捉仅靠单条内容分析难以发现的协同放大行为。结果表明，融合文本信号与网络结构可实现可扩展的虚假信息叙事检测，为理解大规模消息环境中的传播机制提供洞见。
原文摘要 · Abstract (English)
Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content. We propose a graph-based framework for identifying and analyzing disinformation narratives in Telegram ecosystems by combining weak supervision with propagation graph analysis. The approach aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels. This enables the detection of coordinated narrative amplification that is difficult to capture through post-level analysis alone. Our results demonstrate that integrating textual signals with network structure provides a scalable method for detecting disinformation narratives and offers insights into how they propagate within large-scale messaging environments.
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