Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
Summary
This paper proposes a graph-based framework that combines weak supervision with propagation graph analysis to detect and analyze disinformation narratives in Telegram ecosystems, focusing on Russian and Ukrainian channels.
View Cached Full Text
Cached at: 07/15/26, 04:21 AM
# Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels Source: [https://arxiv.org/abs/2607.11894](https://arxiv.org/abs/2607.11894) [View PDF](https://arxiv.org/pdf/2607.11894) > 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\. ## Submission history From: Viktoriia Makovska \[[view email](https://arxiv.org/show-email/150d0a21/2607.11894)\] **\[v1\]**Sat, 9 May 2026 08:35:16 UTC \(519 KB\)
Similar Articles
DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection
Proposes DDGAD, a diffusion-based framework for graph anomaly detection that uses trajectory dynamics to distinguish normal from anomalous nodes, mitigating contamination propagation via a reliability-aware consensus mechanism and three complementary anomaly signals.
LLM-based Detection of Manipulative Political Narratives
A computational framework combining prompt-based filtering and unsupervised clustering to identify manipulative political narrative clusters from social media posts without predefined categories.
TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
TAG-DLM unifies textual reasoning and graph message passing within a masked diffusion language model, enabling joint reasoning over text and graph topology for node classification and link prediction tasks.
Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets
This paper introduces a method that uses LLMs combined with prediction markets to measure how information ecosystems bias strategic beliefs, applying it to Ukraine-related markets and finding that English news sources systematically distort territorial predictions.
A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation
This paper introduces a multi-branch feature fusion framework for detecting and propagating health misinformation, integrating semantic, rhetorical, and psychological cues to achieve strong performance on benchmark datasets.