Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Name
1511.06660.pdf
Description
Accepted version
Size
495.71 KB
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Unknown
Checksum (MD5)
d6425ce693ec27223a081b787014ca4f
Author(s)
Felbo, Bjarke
Sundsøy, Pål
Pentland, Alexander Sandy
Lehmann, Sune
de Montjoye, Yves-Alexandre
Date Issued
2017
Publisher
Springer International Publishing
Citation
Felbo, Bjarke, Sundsøy, Pål, Pentland, Alex ‘Sandy’, Lehmann, Sune and de Montjoye, Yves-Alexandre. 2017. "Modeling the Temporal Nature of Human Behavior for Demographics Prediction."
Version
Author's final manuscript
Abstract
© 2017, Springer International Publishing AG. Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-71273-4_12
