标签尖峰序列中集体注意的局部变化
Local Variation of Collective Attention in Hashtag Spike Trains
摘要：
在本文中，我们提出了一种方法，量化非线性标签时间序列的时间模式。我们的方法是基于神经元尖峰和标签扩散之间的类比。我们采用局部变分，最初开发的神经元尖峰序列的局部时间延迟分析。我们发现，局部变化成功地表征了标签尖峰列车的非线性特征，如突发性和规律性。我们将这种理解应用于一个极端的社会事件中，并能够观察到Twitter用户对该事件的在线集体关注的时间评估。
英文摘要：
In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
