发表机构
Foundation for Research and Technology - Hellas (FORTH); University of Crete（希腊研究与技术基金会; 克里特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于图结构的方法，从已知虚假信息群组出发发现 Telegram 上未知的协同虚假信息频道，识别出 37 个新频道，并揭示高达 80% 的宣传消息及紧密集群传播模式。
AI 中文摘要
近年来，虚假信息在社交网络上日益泛滥。Meta 解雇事实核查人员以及 Twitter 解散信任与安全委员会表明，这一趋势将继续加剧。虽然虚假信息来源（如社交网络账号）有时可以被识别，但新账号每天都在涌现，使得追踪成为一个移动目标。在这项工作中，我们提出了一种基于图的方法来发现此前未知的传播虚假信息的 Telegram 账号。从一组经过验证的虚假信息群组出发，我们检查它们之间的相互联系，并识别出有助于传播虚假叙事的新账号。我们的方法不依赖语言，因为它仅依赖于账号之间的结构关系，而非分析其消息内容。通过这种方法，我们识别出 37 个此前未知的虚假信息频道（增加了两倍）。我们证明 Telegram 频道表现出极其教条化的行为，高达 80% 的消息被标记为宣传内容。我们的发现表明，Telegram 上的虚假信息在紧密互联的集群内传播，在某些情况下，超过 86K 条相同消息被分享到多个频道，这表明存在协同虚假信息活动。
英文摘要
In recent years, disinformation has increasingly proliferated across social networks. The firing of fact-checkers from Meta and the disbanding of Twitter's Trust and Safety Council suggest that this trend will continue to escalate. While disinformation sources (e.g., social network accounts) can sometimes be identified, new accounts emerge daily, making tracking a moving target. In this work, we propose a graph-based methodology to discover previously unknown Telegram accounts that spread disinformation. Starting from a set of verified disinformation groups, we examine their interconnections and identify new accounts that contribute to the dissemination of false narratives. Our approach is language-agnostic, as it relies solely on structural relationships between accounts rather than analyzing their message content. Using this approach, we identify 37 previously unknown disinformation channels (a threefold increase). We demonstrate that Telegram channels display extremely dogmatic behavior with up to 80% of messages being labeled as propaganda. Our findings reveal that misinformation on Telegram spreads within tightly interconnected clusters, in some cases, with over 86K identical messages being shared to multiple channels, suggesting coordinated disinformation campaigns.
Journal refProceedings of the 23rd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-858-7; ISSN 2184-7711, SciTePress, 2026, pages 959-970
