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arXiv · 2610.36571

Peak-DNA: Recurrent Morphology in Global Narrative Propagation

Joosung Kim·Luyonghe Li·Shanwen Wu
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Abstract

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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Joosung Kim, Luyonghe Li, Shanwen Wu. 2026-10-27. Peak-DNA: Recurrent Morphology in Global Narrative Propagation. https://arxiv.org/abs/2610.36571

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