Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on conservative network constructions that erase initially sparse but salient signals. This paper proposes a novel multi-layer framework that captures low-frequency signals of emerging information disorder allowing for locating where online discourse is reframed and amplified over time. The use case is 14 years of Italian discourse on X regarding the Human Papillomavirus (HPV) vaccine across three pivotal epochs (2010-2024). Utilizing hashtag co-occurrence networks, we introduce a dual-layer approach. We first identify robust core discourse coalitions through conservative community detection, revealing a stable prevention-oriented backbone contrasted with increasingly separable skepticism coalitions. We then introduce a coverage layer and project fringe hashtags into core coalitions based on weighted connectivity. Using a manually labelled set of skeptical and conspiratorial seed tweets, we demonstrate that this core-coverage projection significantly improves the recovery of long-tail, problematic hashtags while preserving an interpretable coalition structure. Our findings characterize the structural maturation of polarized narratives and provide a methodology for mapping how discourse is reframed and amplified by information disorder over time.

翻译：理解网络叙事如何通过联盟传播对于识别信息失序至关重要，然而现有的计算分析方法往往依赖保守的网络构建，这可能会抹去初始阶段稀疏但具有显著意义的信号。本文提出一种新型多层框架，能够捕捉新兴信息失序的低频信号，从而定位在线话语随时间推移被重构和放大的节点。以2010-2024年间意大利X平台关于人乳头瘤病毒（HPV）疫苗的三个关键时期的话语为案例，我们利用标签共现网络构建双层分析方法：首先通过保守社区检测识别稳定的核心话语联盟，揭示以预防为导向的稳定主干结构，以及日益分化的怀疑论联盟；随后引入覆盖层，基于加权连通性将边缘标签投射至核心联盟。通过使用人工标注的怀疑论与阴谋论种子推文数据集，我们证明该核心-覆盖投影方法在显著提升长尾问题标签召回率的同时，仍能保持可解释的联盟结构。研究结果揭示了极化叙事的结构化演化特征，并为追踪信息失序如何随时间推移重构和放大话语提供了方法论支撑。