Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
基于图论的俄乌 Telegram 频道间虚假信息叙事传播检测
Abstract: Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content. 摘要： 由于社交媒体上虚假信息传播规模巨大、演变迅速且内容语言多变，检测其中的虚假信息叙事极具挑战性。
We propose a graph-based framework for identifying and analyzing disinformation narratives in Telegram ecosystems by combining weak supervision with propagation graph analysis. 我们提出了一种基于图论的框架，通过结合弱监督学习与传播图分析，旨在识别和分析 Telegram 生态系统中的虚假信息叙事。
The approach aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels. 该方法将语义相关的声明聚类为叙事层级，并对其在互联频道间的传播过程进行建模。
This enables the detection of coordinated narrative amplification that is difficult to capture through post-level analysis alone. 这使得我们能够检测到仅通过单条帖子分析难以捕捉的协同叙事放大行为。
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. 研究结果表明，将文本信号与网络结构相结合，为检测虚假信息叙事提供了一种可扩展的方法，并为理解这些叙事如何在大型消息环境中传播提供了深刻见解。
Paper Details:
- Authors: Yuliia Vistak, Viktoriia Makovska, Vera Schmitt, Veronika Solopova
- arXiv ID: 2607.11894
- Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
- Submission Date: 9 May 2026
论文详情：
- 作者： Yuliia Vistak, Viktoriia Makovska, Vera Schmitt, Veronika Solopova
- arXiv ID: 2607.11894
- 学科分类： 计算与语言 (cs.CL)；人工智能 (cs.AI)
- 提交日期： 2026年5月9日