发表机构
Mathematical Institute, University of Oxford; Institute for New Economic Thinking, University of Oxford; Faculty of Data Science, Shiga University; Graduate School of Frontier Sciences, University of Tokyo（牛津大学数学研究所; 牛津大学新经济思维研究所; 滋贺大学数据科学学部; 东京大学前沿理工学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对社会时间序列的非平稳性，提出基于互相关图与平滑强度函数校正的时间依赖估计方法，可恢复周期性机制中的延迟关系，并在X数据上验证其优于共现度量。
AI 中文摘要
表征社会系统中的时间交互具有挑战性，因为社会行为可能是突发性的且非平稳的，这违反了许多用于测量时间依赖性的方法所依赖的平稳性假设。互相关图是一种用于刻画神经兴奋和抑制的现有技术，它为格兰杰因果关系或共现等方法提供了一种可解释的替代方案，因为它直接从事件时间生成完整的滞后依赖剖面，而不是单一的汇总统计量。我们通过将行为的功能模型与数据驱动的时间响应剖面相结合来改进互相关图。通过刻画周期性结构如何使传统互相关图产生偏差，我们提出了一种基于平滑强度函数的校正方法，该函数可由已知的函数形式指定或通过经验估计。即使在集体节律的时间尺度与所关注交互的时间尺度重叠时，该方法也能提供稳健、可解释的时间依赖剖面估计器。我们通过理论分析和模拟证明，所提出的方法能够恢复周期性机制中的时间依赖关系，优于区间抖动和格兰杰因果关系方法。最后，我们将该方法应用于2019年至2020年间收集的来自X（原推特）的310万个事件时间，展示了互相关图如何揭示共现度量所遗漏的集体在线行为中的延迟时间关系。对于一部分与电视相关的标签，恢复的延迟与已知的播出时间表一致，这提供了证据表明所提出的方法捕获了真实的时间结构，而非共享注意力周期的伪影。
英文摘要
Characterizing temporal interactions in social systems is challenging because social behavior can be bursty and non-stationary, violating the stationarity assumptions of many methods used to measure temporal dependence. The cross-correlogram, an existing technique used to profile neural excitations and inhibitions, offers an interpretable alternative to methods such as Granger causality or co-occurrence, as it produces a full profile of lagged dependence directly from event times rather than a single summary statistic. We adapt the cross-correlogram by integrating functional models of behavior with data-driven temporal response profiling. By characterizing how periodic structure biases traditional cross-correlograms, we propose a correction based on smooth intensity functions, specified from a known functional form or estimated empirically. This approach provides a robust, interpretable estimator of temporal dependency profiles even when collective rhythms operate on timescales that overlap those of the interactions of interest. We demonstrate theoretically and through simulation that the proposed method recovers temporal dependencies in periodic regimes, outperforming interval-jitter and Granger causality methods. Finally, we apply the method to 3.1 million event times from X (formerly Twitter) collected between 2019 and 2020, demonstrating how cross-correlograms reveal delayed temporal relationships in collective online behavior that are missed by co-occurrence measures. For a subset of television-related hashtags, recovered delays align with known broadcast schedules, providing evidence that the proposed method captures genuine temporal structure rather than artifacts of shared attention cycles.
