Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
This paper proposes a graph-based framework combining weak supervision with propagation graph analysis for detecting disinformation narratives in Telegram ecosystems. It aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels, enabling scalable detection of coordinated amplification.
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[Submitted on 9 May 2026]
Title:Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
View a PDF of the paper titled Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels, by Yuliia Vistak and 3 other authors
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Abstract: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.
Comments: UNLP 2026 The Fifth Ukrainian Natural Language Processing Conference
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11894 [cs.CL]
(or arXiv:2607.11894v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.11894
arXiv-issued DOI via DataCite
Submission history
From: Viktoriia Makovska [view email] [v1] Sat, 9 May 2026 08:35:16 UTC (519 KB)
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