Abstract We examine conversations on Telegram during the 2024 U.S. elections to understand how political narratives emerge and cluster at scale. We propose a general-purpose pipeline that combines message-level topic modeling with co-forwarding graph analysis to filter thematically relevant chats. LLM-based daily summarization and encoding are then applied to detect topics and trace the dynamics of chat attention over time in large-scale conversational datasets. Applied to 486 M messages, our method isolates politically engaged groups and detects 36 refined topics active during June–July 2024. We uncover cohesive thematic spheres-clusters of chats with synchronized attention and selective content sharing-that include ideologically extreme or conspiratorial niches. The framework generalizes beyond this case, providing a scalable tool for studying narrative alignment in messaging platforms and social networks.
Paoletti et al. (Tue,) studied this question.