用随机微分方程建模的推特上的突发用户行为。
Emergent user behavior on Twitter modelled by a stochastic differential equation.
作者信息
Mollgaard Anders, Mathiesen Joachim
机构信息
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.
出版信息
PLoS One. 2015 May 8;10(5):e0123876. doi: 10.1371/journal.pone.0123876. eCollection 2015.
Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.
来自社交媒体网站推特的数据被用于研究品牌名称推文率的波动情况。推文率是一种高度相关的用户行为的结果，这种行为会导致具有特征1/f噪声的突发集体动态。在此，我们使用对品牌名称的聚合“用户兴趣”，通过一个带有乘性噪声的随机微分方程来对人类集体动态进行建模。该模型得到了对推文率波动的详细分析的支持，并且它再现了数据中发现的精确突发动态以及1/f噪声。
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