Inferring social ties from geographic coincidences
Inferring social ties from geographic coincidences
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DOI：
10.1073/pnas.1006155107
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发表时间：
2010-12-28
影响因子：
11.1
通讯作者：
Kleinberg, Jon
中科院分区：
文献类型：
--
作者：
Crandall, David J.;Backstrom, Lars;Kleinberg, Jon
我们调查了人们之间的社会联系在多大程度上可以从时间和空间上的共同出现中推断出来：考虑到两个人在大约相同的时间，在多个场合，在大约相同的地理位置，他们有多大可能认识对方？此外，这种可能性如何取决于同现的空间和时间接近度？此类问题出现在源自在线和离线域的数据以及捕获在线和离线行为之间的接口的设置中。在这里，我们开发了一个框架来量化这些问题的答案，我们将这个框架应用到社交媒体网站的公开数据中，发现即使是非常少量的同现也会导致社会关系的经验可能性很高。然后，我们提出的概率模型，显示如何这样大的概率可以产生一个自然的模型，在社会关系的存在下，接近和同现。除了提供一种方法来建立这些措施的一些第一个可量化的估计，我们的研究结果有潜在的隐私影响，特别是在社会结构的方式，可以从公共在线记录，捕捉个人的物理位置随着时间的推移推断。
We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals' physical locations over time.