We uncover a universal scaling law governing the dispersion of collective attention and identify its underlying stochastic criticality. By analysing large-scale ensembles of Wikipedia page views, we find that the variance of logarithmic attention grows ultraslowly, $\operatorname{Var}[\ln{X(t)}]\propto\ln{t}$, in sharp contrast to the power-law scaling typically expected for diffusive processes. We show that this behaviour is captured by a minimal stochastic differential equation driven by fractional Brownian motion, in which long-range memory ($H$) and temporal decay of volatility ($η$) enter through the single exponent $ξ\equiv H-η$. At marginality, $ξ=0$, the variance grows logarithmically, marking the critical boundary between power-law growth ($ξ>0$) and saturation ($ξ<0$). By incorporating article-level heterogeneity through a Gaussian mixture model, we further reconstruct the empirical distribution of cumulative attention within the same framework. Our results place collective attention in a distinct class of non-Markovian stochastic processes, with close affinity to ageing-like and ultraslow dynamics in glassy systems.

翻译：我们揭示了支配集体注意力弥散性的普适标度律，并识别了其内在的随机临界性。通过分析大规模维基百科页面浏览量的集合数据，我们发现对数注意力的方差呈超慢增长，即 $\operatorname{Var}[\ln{X(t)}]\propto\ln{t}$，这与扩散过程通常预期的幂律标度形成鲜明对比。我们证明该行为可由一个由分式布朗运动驱动的极小随机微分方程所刻画，其中长程记忆（$H$）与波动率的时间衰减（$η$）通过单一指数 $ξ\equiv H-η$ 共同作用。在临界点 $ξ=0$ 处，方差呈对数增长，标志着幂律增长（$ξ>0$）与饱和（$ξ<0$）之间的临界边界。通过引入高斯混合模型以纳入文章层面的异质性，我们在同一框架内进一步重构了累积注意力的经验分布。我们的研究结果将集体注意力归入一类特殊的非马尔可夫随机过程，其与玻璃态系统中的类老化及超慢动力学具有紧密的亲和性。