Unraveling the Italian and English Telegram Conspiracy Spheres through Message Forwarding

TL;DR

This study investigates how conspiratorial narratives propagate on Telegram through message forwarding in Italian and English communities. It constructs directed weighted forwarding graphs $ abla{\mathcal{G}}=(\mathcal{N},\mathcal{E})$ with edge weights $w_{e_{u,v}}$ to model content diffusion across chats, and applies CorEx topic modeling alongside $t$-SNE visualization to characterize narratives within communities. Two large datasets are collected via seed-based snowball sampling from forward links, yielding $|\,\mathcal{N}_{IT}|=1{,}346$ Italian chats with 3.4M messages and $|\,\mathcal{N}_{EN}|=634$ English chats with 5M messages, including channels, groups, and linked chats. The analysis uncovers distinct Italian versus English conspiracy landscapes—Italian themes span diverse sources and religiosity, while English narratives cluster around QAnon and related topics—and demonstrates robustness to seed selection, offering a generalizable framework for studying misinformation diffusion on Telegram.

Abstract

Telegram has grown into a significant platform for news and information sharing, favored for its anonymity and minimal moderation. This openness, however, makes it vulnerable to misinformation and conspiracy theories. In this study, we explore the dynamics of conspiratorial narrative dissemination within Telegram, focusing on Italian and English landscapes. In particular, we leverage the mechanism of message forwarding within Telegram and collect two extensive datasets through snowball strategy. We adopt a network-based approach and build the Italian and English Telegram networks to reveal their respective communities. By employing topic modeling, we uncover distinct narratives and dynamics of misinformation spread. Results highlight differences between Italian and English conspiracy landscapes, with Italian discourse involving assorted conspiracy theories and alternative news sources intertwined with legitimate news sources, whereas English discourse is characterized by a more focused approach on specific narratives such as QAnon and political conspiracies. Finally, we show that our methodology exhibits robustness across initial seed selections, suggesting broader applicability. This study contributes to understanding information and misinformation spread on Italian and English Telegram ecosystems through the mechanism of message forwarding

Figures (8)

• Figure 1: IT
• Figure 2: EN
• Figure 4: Users
• Figure 5: Messages
• Figure 7: t-SNE representation of message distribution by topic in the EN Dataset