Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country's long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.

翻译：相变，即组织宏观模式之间的突变，是复杂系统中的普遍现象。尽管在物理和自然科学领域已有大量研究，但社会系统中这一现象的实证研究仍相对不足。本研究旨在探索集体内乱的动态变化是否可合理表征为一系列循环相移过程，且每个阶段均具有可测量、可识别的潜在特征。基于此前将内乱视为自组织临界系统的研究成果，我们引入了一个宏观层面的内乱统计模型，并利用涵盖1946年至2017年全球170个国家内乱事件的综合数据集评估其有效性。研究结果表明，宏观相模型能有效刻画全球不同国家内乱数据的特征，且内乱动态的某些方面可能存在普适机制。我们还提出了一种量化国家单位时间内长期内乱程度的标度，并揭示了内乱事件在地理上呈集聚趋势，其强度集中于特定区域。该方法具有超越内乱范畴、识别并测量各类集体人类现象中相变的潜力，有助于深化对复杂社会系统的理解。