AI 中文总结
研究社交媒体虚假信息叙事检测难题，提出基于图的框架，结合弱监督与传播图分析，聚合语义相关断言建模传播，可检测协调叙事放大及虚假信息叙事传播，提供可扩展检测方法。
AI 中文摘要
由于在线内容的传播规模、快速演变和语言可变性，检测社交媒体上的虚假信息叙事具有挑战性。我们提出了一个基于图的框架，用于通过将弱监督与传播图分析相结合，在Telegram生态系统中识别和分析虚假信息叙事。该方法将语义相关的断言聚合到叙事级别的簇中，并对它们在相互连接的频道中的传播进行建模。这使得能够检测仅通过帖子级分析难以捕获的协调叙事放大。我们的结果表明，将文本信号与网络结构相结合提供了一种可扩展的方法来检测虚假信息叙事，并深入了解它们在大规模消息传递环境中的传播方式。
英文摘要
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.
CommentsUNLP 2026 The Fifth Ukrainian Natural Language Processing Conference
