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Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats: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\u00a0M messages, our method isolates politically engaged groups and detects 36 refined topics active during June\u2013July 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.<\/jats:p>","DOI":"10.1007\/s13278-025-01504-0","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T04:21:37Z","timestamp":1756182097000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tracing the 2024 U.S. election debate on Telegram with LLMs and graph analysis"],"prefix":"10.1007","volume":"15","author":[{"given":"Giordano","family":"Paoletti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos H. 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