Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
IEEE Pervasive Computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Social fMRI: Investigating and shaping social mechanisms in the real world
Pervasive and Mobile Computing
Journal of the American Society for Information Science and Technology
Mining smartphone data to classify life-facets of social relationships
Proceedings of the 2013 conference on Computer supported cooperative work
Detecting anomalous behaviors using structural properties of social networks
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Visual analysis of social networks in space and time using smartphone logs
Pervasive and Mobile Computing
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As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies.