发表机构
Institute of Science Tokyo; Hirosaki University; University of Aizu; The University of Tokyo（东京科学大学; 弘前大学; 会津大学; 东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过谱非正态性量化X平台约58,000个级联网络，发现非正态性与峰值集中度强相关，且中期结构预测准确率可超80%，为信息扩散预测提供了新基准。
AI 中文摘要
社交媒体上的信息级联通常被概念化为有向的前馈分支过程。然而，由于局部聚类、互惠评论和多波次时间激增，现实世界中的扩散路径常常偏离纯层级树状结构。在本研究中，我们利用谱非正态性（通过Henrici的正态偏离度衡量）量化了X（原Twitter）上经验信息级联的方向不对称性和层级结构。通过分析涵盖不同主题（包括政治、娱乐、自然灾害等）的约58,000个级联网络，我们研究了（1）非正态性与时间动态（如内源型与外源型模式以及爆发性）之间的关系，（2）非正态性是否与级联的峰值集中度或整体规模相关，（3）级联网络结构的整体非正态性是否可以从其早期阶段进行预测。我们发现，非正态性与峰值集中度（峰值/节点总数）强烈相关，而非整体级联规模，这表征了由快速、不对称转发主导的级联。此外，虽然早期阶段的结构预测（观察到≤30%的节点）表现出预期的基线不确定性（在±20%误差容限下准确率为51%-72%），但可预测性在中间增长阶段迅速巩固，一旦观察到50%-60%的网络，所有动态聚类的准确率均超过80%。通过识别级联结构的拓扑和动态相关性，本研究推进了对信息流的理解，并为预测有向扩散架构建立了可量化的基准。
英文摘要
Information cascades on social media are conventionally conceptualized as directed, feedforward branching processes. However, real-world diffusion pathways frequently deviate from pure hierarchical trees due to localized clustering, reciprocal commentary, and multi-wave temporal surges. In this work, we quantify the directional asymmetry and hierarchical structure of empirical information cascades on X (formerly Twitter) using spectral non-normality via Henrici's departure from normality. Analyzing approximately 58,000 cascade networks across diverse topics (including politics, entertainment, natural disasters, etc.), we investigate (1) how non-normality relates to temporal dynamics such as endogenous-like versus exogenous-like patterns and burstiness, (2) whether non-normality is correlated with the peak concentration or overall size of a cascade, (3) whether the overall non-normality of a cascade's network structure can be predicted from its early stages. We find that non-normality strongly aligns with peak concentration (peak/N) rather than overall cascade size, characterizing cascades governed by rapid, asymmetric forwarding. Furthermore, while early-stage structural forecasting (<= 30% of nodes observed) exhibits expected baseline uncertainty (51%-72% accuracy at a +/- 20% error tolerance), predictability consolidates rapidly during intermediate growth, exceeding 80% across all dynamic clusters once 50%-60% of the network is observed. By identifying the topological and dynamic correlates of cascade structures, this study advances our understanding of information flow and establishes a quantifiable benchmark for forecasting directional diffusion architectures.
Comments18 pages, 5 figures
