发表机构
PK Krakow University of Technology; Institute of Nuclear Physics, Polish Academy of Sciences; Jagiellonian University（克拉科夫理工大学; 波兰科学院核物理研究所; 雅盖隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究发现部分Twitch聊天展现多重分形特征，通过MFDFA等方法分析其时间组织，表明其类似意识流叙事，将直播聊天动力学纳入复杂人类信号物理研究范畴。
AI 中文摘要
直播平台会生成集体人类活动的高频记录，其中大量松散协作的用户会对共享外部刺激及彼此做出实时反应。本研究调查Twitch聊天消息的时间组织是否展现出复杂动力系统特有的多重分形特征。选取高活跃度聊天内容，通过测量每条连续消息的单词长度（平台表情符号视为单个标记）将其转换为时间序列。所得信号呈现出异质波动、突发式组织及重尾分布，在若干案例中，q-威布尔（q-Weibull）形式比传统轻尾模型能更好地捕捉这些特征。自相关分析显示，在广泛的消息滞后范围内存在持续的时间依赖关系，表明这种变异性并非仅由独立的消息生成产生。多重分形去趋势波动分析（MFDFA）表明，部分聊天具有发育良好的多重分形谱，且广义赫斯特指数强烈依赖于矩阶数，而其他聊天则表现出弱标度、近似单分形行为或无明确标度区域。替代测试显示，时间相关性对观测到的多重分形性至关重要，而重尾分布会增强其表观强度。因此，在特定动力条件下，Twitch聊天可能形成一种涌现的集体信号，其碎片化、反应性、联想性及多尺度组织在结构上类似意识流叙事。该表述仅作为结构和动力类比，并非主张存在统一的集体心智，也不主张与文学意识流写作在语义、认知或现象学上等价。研究结果将直播聊天动力学置于更广泛的复杂、间歇性、人类生成信号的物理学框架中。
英文摘要
Live-streaming platforms generate high-frequency records of collective human activity in which many loosely coordinated users react in real time to shared external stimuli and to one another. We investigate whether the temporal organization of Twitch chat messages exhibits multifractal signatures characteristic of complex dynamical systems. Selected high-activity chats were converted into time series by measuring the length of each consecutive message in words, with platform emotes treated as individual tokens. The resulting signals display heterogeneous fluctuations, bursty organization, and heavy-tailed distributions that, in several cases, are better captured by q-Weibull forms than by conventional light-tailed models. Autocorrelation analysis reveals persistent temporal dependence over broad ranges of message lags, indicating that the variability is not produced by independent message generation alone. MFDFA shows that some chats possess well-developed multifractal spectra and generalized Hurst exponents strongly dependent on moment order, whereas others exhibit weak scaling, approximately monofractal behavior, or no well-defined scaling regime. Surrogate tests indicate that temporal correlations are essential for the observed multifractality, while heavy tails enhance its apparent strength. Under particular dynamical conditions, Twitch chat may therefore form an emergent collective signal whose fragmented, reactive, associative, and multiscale organization is structurally reminiscent of stream-of-consciousness narrative. This expression is used strictly as a structural and dynamical analogy, not as a claim of a unified collective mind or of semantic, cognitive, or phenomenological equivalence with literary stream-of-consciousness writing. The results position live-stream chat dynamics within the broader physics of complex, intermittent, human-generated signals.
Commentsaccepted for publication in CHAOS: An Interdisciplinary Journal of Nonlinear Science
