Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram

2026-09-22

AI Summary

This study addresses the covert dissemination of coordinated disinformation on Telegram and the challenge of tracking newly created accounts by proposing a language-agnostic graph structural analysis method. Leveraging graph neural networks, social network structural analysis, and unsupervised anomaly detection, the approach identifies coordinated propagation nodes by mining topological interconnections among known inauthentic accounts, thereby overcoming the limitations of traditional text-based content moderation. The proposed framework successfully uncovers 37 previously unknown malicious channels, expanding the scale of identified adversarial nodes threefold. Empirical results reveal highly coordinated propagation clusters, demonstrating that this method provides an effective paradigm for detecting coordinated inauthentic behavior across multilingual social platforms.

Abstract

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.

Problem

• disinformation
• Telegram
• coordinated campaigns
• social networks
• misinformation detection

Innovation

• Graph-based methodology
• Coordinated disinformation campaigns
• Language-agnostic
• Telegram networks
• Structural relationships