Toxic Discourse in the Digital Battlefield: Analysing Telegram Channels During the Russia–Ukraine ‘Conflict’

Abstract

Instant messenger Telegram has emerged as a favoured platform for far-right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre-trained language models to detect and measure toxicity in political content on Telegram channels. The proposed techniques have shown notable advancements in identifying toxic information using a fine-tuned RoBERTa model. Through the combination of data analysis, time-series analysis, and BERTopic modelling, the research demonstrates how toxicity varies by topic, country, and time period, using metadata. The study identified key topics in the dataset, which includes 23.6 million messages from 1491 Telegram channels, including the Russian–Ukrainian conflict and political tensions in Europe and the United States from 2016 to 1 July 2023. Despite these achievements, challenges such as the dominance of Russian language content and a focus on specific topics were highlighted. This research advances the understanding of how toxic language and propaganda are disseminated across different languages and political narratives, contributing to the study of digital communication and information warfare.