Telegram上传播宣传账号的特征刻画与检测
Characterizing and Detecting Propaganda-Spreading Accounts on Telegram
AI总结：
针对Telegram频道和群组中的宣传传播问题，本文分析1700余万条消息并识别两个协同宣传网络，提出利用合法消息与宣传回复关系的检测机制，仅需一条消息即可达到97.6%的检测率。
摘要：
社交媒体上的虚假信息行动和宣传等基于信息的攻击，正成为新兴的网络安全威胁。安全界一直专注于应对X和Reddit等社交媒体平台上的这些威胁。然而，它们也出现在WhatsApp、Telegram和Signal等即时通讯社交媒体平台上。在这些平台中，基于信息的攻击主要发生在群组和频道中，需要频道管理员进行人工审核。我们收集、标注并分析了一个包含超过1700万条Telegram评论和消息的大型数据集。分析揭示了两个相互独立、协同运作的网络，分别传播亲俄和亲乌克兰宣传，并获得真实用户的回复。我们提出了一种新颖的宣传检测机制，该机制利用合法用户消息与宣传回复之间的关系，并针对Telegram向审核人员提供的信息进行定制。仅查看某账号的一条消息后，我们的方法就比人工审核员更快、成本更低，且检测率（97.6%）高出11.6个百分点。即使宣传内容不断演变，该方法仍然有效。
英文摘要：
Information-based attacks on social media, such as disinformation campaigns and propaganda, are emerging cybersecurity threats. The security community has focused on countering these threats on social media platforms like X and Reddit. However, they also appear in instant-messaging social media platforms such as WhatsApp, Telegram, and Signal. In these platforms information-based attacks primarily happen in groups and channels, requiring manual moderation efforts by channel administrators. We collect, label, and analyze a large dataset of more than 17 million Telegram comments and messages. Our analysis uncovers two independent, coordinated networks that spread pro-Russian and pro-Ukrainian propaganda, garnering replies from real users. We propose a novel mechanism for detecting propaganda that capitalizes on the relationship between legitimate user messages and propaganda replies and is tailored to the information that Telegram makes available to moderators. Our method is faster, cheaper, and has a detection rate (97.6%) 11.6 percentage points higher than human moderators after seeing only one message from an account. It remains effective despite evolving propaganda.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
