Telegram as a Battlefield: Kremlin-related Communications during the Russia-Ukraine Conflict

2025-01-03

AI Summary

During the Russia-Ukraine conflict, Telegram exhibited pronounced asymmetry and rampant disinformation in pro- versus anti-Kremlin information dissemination. To address this, we construct the first large-scale, dual-perspective Telegram dataset spanning one year pre- and one year post-invasion, comprising 404 pro-Kremlin and 114 anti-Kremlin channels, with over 5.22 million original posts. Our methodology introduces the first long-term, symmetric, and systematic source collection and expert annotation pipeline for such content, alongside a novel multi-stage cleaning and metadata enrichment framework tailored to low-regulation platforms. Data acquisition leverages the Telegram API and third-party tools; analysis integrates multilingual NLP techniques and spatiotemporal statistical modeling. The fully annotated dataset—including complete acquisition logs and channel inventories—is publicly released. It serves as critical infrastructure for studying disinformation propagation mechanisms, geopolitical discourse evolution, and crisis response modeling.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and potential misinformation were spread on Telegram, while anti-Kremlin narratives with related content were also propagated, such as war footage, troop movements, maps of bomb shelters, and air raid warnings. This paper presents a dataset of posts from both pro-Kremlin and anti-Kremlin Telegram channels, collected over a period spanning a year before and a year after the Russian invasion. The dataset comprises 404 pro-Kremlin channels with 4,109,645 posts and 114 anti-Kremlin channels with 1,117,768 posts. We provide details on the data collection process, processing methods, and dataset characterization. Lastly, we discuss the potential research opportunities this dataset may enable researchers across various disciplines.

Problem

• Information Imbalance
• Misinformation
• Telegram Platform

Innovation

• Telegram Dataset
• Russia-Ukraine Conflict
• Pro-and Anti-Government Channels