Modeling human behavior at a large scale

Until recently, complex phenomena—such as human behavior and disease epidemics—have been modeled primarily at an aggregate level. Detailed studies have been limited to small domains encompassing only a few subjects, as scaling the methods involved poses considerable challenges in terms of cost, human effort required, computational bottlenecks, and data sources available. With the surge of online social media and sensor networks, the abundance of interesting and publicly accessible data is beginning to increase. However, we also need the ability to reason about it efficiently. The underlying theme of this thesis is the unification and data mining of diverse, noisy, and incomplete sensory data over large numbers of individuals. We show that the mined patterns can be leveraged in predictive models of human behavior and other phenomena at a large scale. We find that raw sensory data linked with the content of users' online communication, the explicit as well as the implicit online social interactions, and interpersonal relationships are rich information sources upon which strong machine learning models can be built. Example domains where such models apply include understanding human activities, predicting people's location and social ties from their online behavior, and predicting the emergence of global epidemics from day-to-day interpersonal interactions.