Twitter上的紧急用户行为的随机微分方程模型
Emergent user behavior on Twitter modelled by a stochastic differential equation
摘要：
来自社交媒体网站Twitter的数据被用来研究品牌名称推文率的波动。推文率是强烈相关的用户行为的结果，这导致了具有特征1/f噪声的突发集体动态。在这里，我们使用聚合的“用户兴趣”在一个品牌名称的集体人类动力学模型的随机微分方程乘性噪声。该模型得到了鸣叫率波动的详细分析的支持，它再现了数据和1/f噪声中发现的精确的突发动态。
英文摘要：
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.
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