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<p>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&#xa0;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.</p>

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Tracing the 2024 U.S. election debate on Telegram with LLMs and graph analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Giordano Paoletti,
  • Carlos H. G. Ferreira,
  • Luca Vassio,
  • Leonardo Rocha,
  • Jussara M. Almeida

摘要

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.