Using Deep Learning to Predict Demographics from Mobile Phone Metadata

Bjarke Felbo, Pål Sundsøy, Alex 'Sandy' Pentland, Sune Lehmann, Yves-Alexandre de Montjoye

CMT Id: 296

Abstract: Mobile phone metadata are increasingly used to study human behavior at large-scale. There has recently been a growing interest in predicting demographic information from metadata. Previous approaches relied on hand-engineered features. We here apply, for the first time, deep learning methods to mobile phone metadata using a convolutional network. Our method provides high accuracy on both age and gender prediction. These results show great potential for deep learning approaches for prediction tasks using standard mobile phone metadata.

Conflicts: mit.edu, dtu.dk, harvard.edu, ku.dk, telenor.com, telenor.no