会话伙伴的可预测性
Predictability of conversation partners
摘要：
传感技术的最新发展使我们能够更详细地研究人类社会行为的本质。通过将信息论方法应用于手机位置的时空数据，[C。Song等人，Science 327，1018（2010）]发现人类流动模式是显著可预测的。受他们工作的启发，我们在另一种人类社会活动中解决了类似的可预测性问题：对话事件。一个人的会话伙伴的序列中的可预测性被定义为在给定当前伙伴的情况下，一个人的下一个会话伙伴可以被预测的程度。我们通过使用互信息来量化这种可预测性。我们使用从日本两家公司办公室收集的面对面互动的纵向数据，研究每个人的会话事件的可预测性。每个主体佩戴配备有红外传感器节点的姓名标签，并且当在紧密接近的传感器节点之间交换信号时，会话事件被标记。我们发现，对话事件在一定程度上是可预测的;知道当前的合作伙伴减少了对下一个合作伙伴的不确定性平均28.4%。大部分的可预测性解释的长尾分布的间隔时间。然而，除了其长尾性质的贡献外，数据中也存在可预测性。此外，个人的可预测性与从数据导出的静态社交网络中的位置相关。被限制在一个社区中的个体--从周围有大量三角形的意义上说--往往具有较低的可预测性，而那些连接不同社区的个体往往具有较高的可预测性。
英文摘要：
Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information theoretic method to the spatiotemporal data of cell-phone locations, [C. Song et al. Science 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to some extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position in the static social network derived from the data. Individuals confined in a community - in the sense of an abundance of surrounding triangles - tend to have low predictability, and those bridging different communities tend to have high predictability.
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