How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage
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Pentland_How many.pdf
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Author(s)
Altshuler, Yaniv
Fire, Michael
Aharony, Nadav
Elovici, Yuval
Pentland, Alex Paul
Date Issued
April 2012
Journal
Social Computing, Behavioral - Cultural Modeling and Prediction
Publisher
Springer Berlin Heidelberg
Citation
Altshuler, Yaniv, Michael Fire, Nadav Aharony, Yuval Elovici, and Alex Pentland. How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage. LNCS Vol. 7227, 2012, Springer-Verlag, 2012. 43-52
Version
Author's final manuscript
Abstract
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.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-29047-3_6
