Telegram向けプロパガンダ検出メカニズムを開発(First propaganda detection mechanism for Telegram)

2025-08-18 スイス連邦工科大学ローザンヌ校(EPFL)

EPFLなどの研究チームは、Telegram上のプロパガンダを自動検出する初の手法を開発した。1,370万件のコメントを分析し、約1.8%がプロパガンダと判定。親ロシア系が主で、投稿の最大5%を占めた。特徴は、会話を始めず他者の投稿に反応し、同内容を複数回投稿する点。このパターンを用い、単一コメントでも97.6%の高精度で検出可能に。これは人間のモデレーターよりも高い精度を示し、Telegram上の情報操作対策として注目される。

Telegram向けプロパガンダ検出メカニズムを開発(First propaganda detection mechanism for Telegram)In this diagram, each dot represents a Telegram account. A line indicates that an account has replicated a comment from another account with the same wording. The diagram shows that propaganda accounts (right) behave very differently to normal accounts (left). They create networks that repost the same content over and over again. © 2025 EPFL / Ruhr University

<関連情報>

テレグラムにおけるプロパガンダ拡散アカウントの特性評価と検出 Characterizing and Detecting Propaganda-Spreading Accounts on Telegram

Klim Kireev, Yevhen Mykhno, Carmela Troncoso, Rebekah Overdorf
arXiv  Submitted on 12 Jun 2024
DOI:https://doi.org/10.48550/arXiv.2406.08084

Abstract

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.

1603情報システム・データ工学
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