Niels Bohr Institute, University of Copenhagen, DK-2100 Copenhagen, Denmark.
Proc Natl Acad Sci U S A. 2013 Oct 22;110(43):17259-62. doi: 10.1073/pnas.1304179110. Epub 2013 Oct 7.
Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a power spectrum. We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrum are fully consistent with the empirical observations.
利用社交媒体网站（Twitter）和金融证券交易数据的实证数据，我们分析了大规模社会组织中的相关人类活动。这种活动通常由现实世界事件激发，通过国际品牌名称和交易数量的出现率来衡量，其特征是间歇性波动，高活动爆发与平静期交替出现。这些波动呈广泛分布，具有立方反比尾部，并且具有随时间变化的幂律谱。我们通过随机点过程来描述活动，并从相应的随机微分方程推导出活动水平的分布。该分布和相应的幂律谱与经验观测完全一致。
