# Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels

arXiv: https://arxiv.org/abs/2607.11894 · HTML: https://mlsanity.com/paper/2607.11894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 11 of 11)

- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles](https://mlsanity.com/paper/2406.12614/) (2024)
- [TGDataset: Collecting and Exploring the Largest Telegram Channels Dataset](https://mlsanity.com/paper/2303.05345/) (2023)
- [Retrieving Climate Change Disinformation by Narrative](https://mlsanity.com/paper/2603.22015/) (2026)
- [MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages](https://mlsanity.com/paper/2506.22529/) (2025)
- [Evolution of wartime discourse on Telegram: A comparative study of Ukrainian and Russian policymakers' communication before and after Russia's full-scale invasion of Ukraine](https://mlsanity.com/paper/2510.11746/) (2025)

## Abstract (arXiv)

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.