A probabilistic approach to socio-geographic reality mining

Abstract

As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society. For example, by understanding a community's movements and interactions, appropriate measures may be put in place to prevent the threat of an epidemic. The study of such human-centric massive datasets requires advanced mathematical models and tools. In this thesis, we investigate probabilistic topic models as unsupervised machine learning tools for large-scale socio-geographic activity mining.

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