Signals from the crowd: uncovering social relationships through smartphone probes

M. Barbera, Alessandro Epasto, A. Mei, V. Perta, Julinda Stefa
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引用次数: 120

Abstract

The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?". In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants---language, vendor adoption, and so on.
来自人群的信号:通过智能手机探测器揭示社会关系
WiFi接入点的日益普及,再加上智能手机的普及,表明随时随地上网将很快(如果不是已经)成为现实。即使在3G连接的情况下,我们的设备也会自动切换到WiFi网络,以改善用户体验。大多数情况下,这是通过向已知接入点(ap)反复广播自动连接请求(称为探测请求)来实现的,例如,“家庭WiFi”、“校园WiFi”等。在一大群人的聚会中,这些探针的数量可能非常高。这种情况引出了一个自然的问题:“能否通过智能手机探测器推断出一大群人的社会结构及其社会经济地位的重要信息?”在这项工作中,我们对这个问题给出了肯定的答案。我们组织了一个长达3个月的活动,在此期间,我们收集了超过16万台不同设备发送的大约1100万个探测器。在竞选期间,我们针对的是吸引大量人群以及其他人群聚集的国内和国际活动。然后,我们提出了一种简单而自动的方法来构建智能手机用户的潜在社交图谱,从他们的探针开始。我们对每个目标事件都这样做,发现它们都具有社交网络属性。此外,我们还表明,通过查看事件中的探测,我们可以了解其参与者的重要社会学方面——语言、供应商采用,等等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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