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We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially(More)
Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate(More)
In this work we identify the structure inherent in daily human behavior with models that can accurately analyze, predict and cluster multimodal data from individuals and groups. We represent this structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an(More)
The majority of humans today carry mobile telephones. These phones automatically capture behavioral data from virtually every human society, stored in service provider databases around the world. This article discusses the different types of data captured and how they can be used to provide insight into human cultures. Examples are provided from a variety(More)
Over one billion people live in the world's 200,000 slums and informal settlements. We used data generated from mobile phones to better understand one of the largest slums, Kibera located in Nairobi, Kenya. Using call logs from June 2008-June 2009 and theories from human geography, economics, sociology, journalists , and anthropologists as a basis, we(More)
We introduce a method for situation understanding from natural, face-to-face conversation. Our method combines a network of commonsense knowledge with keyword spotting and contextual information automatically obtained from a wearable device such as a PDA or cell phone. Using this method we demonstrate the potential for high accuracy, detailed classification(More)