← 研踪 PaperTrailarXiv:2610.36571 · v1
Peak-DNA: Recurrent Morphology in Global Narrative Propagation
首次提交 2026-10-27 11:21
版本更新 2026-10-27 11:21
雷达观测 2026-10-29 00:35
摘要
Global narrative streams exhibit noisy, fragmented, and asynchronous amplification bursts, making direct waveform comparison unreliable for recurrence analysis. This paper introduces Peak-DNA, a framework for identifying recurrent propagation morphology in global narrative streams. The proposed framework transforms hourly GDELT activity into normalized macro-peak episodes through denoising, macro-peak consolidation, and temporal alignment, and compares propagation morphology using DTW-based similarity analysis. Using GDELT narrative streams, we analyze five high-amplification narrative domains: Iran, Defense, AI, Space/Elon, and Quantum/PQC. The results show that amplification events repeatedly converge toward morphology-level propagation patterns within each narrative domain. DTW-based similarity analysis further demonstrates strong intra-domain structural compactness despite heterogeneous publication dynamics and severe observational noise. These findings suggest a measurable morphology layer beyond semantic content or anomaly intensity, providing an initial structural foundation for recurrence-aware narrative monitoring, propagation family discovery, and longitudinal Narrative Genome analysis.
作者备注：8 pages. Accepted and presented at ASONAM 2026, Rabat, Morocco, August 24-27, 2026. Proceedings publication forthcoming
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v1 · 2026-10-27 11:21 · 最新观测版本
Peak-DNA: Recurrent Morphology in Global Narrative Propagation
Global narrative streams exhibit noisy, fragmented, and asynchronous amplification bursts, making direct waveform comparison unreliable for recurrence analysis. This paper introduces Peak-DNA, a framework for identifying recurrent propagation morphology in global narrative streams. The proposed framework transforms hourly GDELT activity into normalized macro-peak episodes through denoising, macro-peak consolidation, and temporal alignment, and compares propagation morphology using DTW-based similarity analysis. Using GDELT narrative streams, we analyze five high-amplification narrative domains: Iran, Defense, AI, Space/Elon, and Quantum/PQC. The results show that amplification events repeatedly converge toward morphology-level propagation patterns within each narrative domain. DTW-based similarity analysis further demonstrates strong intra-domain structural compactness despite heterogeneous publication dynamics and severe observational noise. These findings suggest a measurable morphology layer beyond semantic content or anomaly intensity, providing an initial structural foundation for recurrence-aware narrative monitoring, propagation family discovery, and longitudinal Narrative Genome analysis.
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