What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data

We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including going to work at 10am", leaving work at night", or staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.

Katayoun Farrahi, Daniel Gatica-Perez

staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as

being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.

IDIAP2008

Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models

Katayoun Farrahi, Daniel Gatica-Perez

In this work we discover the daily location-driven routines which are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16 month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include "going to work late", "going home early", "working non-stop" and "having no reception (phone off)" at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as "going to work early" or "turning off the mobile (or having no reception) in the evenings". We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.

2011

A Probabilistic Approach to Socio-Geographic Reality Mining

Katayoun Farrahi

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. We first investigate two types of probabilistic topic models for large-scale location-driven phone data mining. We propose a methodology based on Latent Dirichlet Allocation, followed by the Author Topic Model, for the discovery of dominant location routines mined from the MIT Reality Mining data set containing the activities of 97 individuals over the course of a 16 month period. We investigate the many possibilities of our proposed approach in terms of activity modeling, including differentiating users with high and low varying lifestyles and determining when a user's activities fluctuate from the norm over time. We then consider both location and interaction features from cell tower connections and Bluetooth, in single and multimodal forms for routine discovery, where the daily routines discovered contain information about the interactions of the day in addition to the locations visited. We also propose a method for the prediction of missing multimodal data based on Latent Dirichlet Allocation. We further consider a supervised approach for day type and student type classification using similar socio-geographic features. We then propose two new probabilistic approaches to alleviate some of the limitations of Latent Dirichlet Allocation for activity modeling. Large duration activities and varying time duration activities can not be modeled with the initially proposed methods due to problems with input and model parameter size explosion. We first propose a Multi-Level Topic Model as a method to incorporate multiple time duration sequences into a probabilistic generative topic model. We then propose the Pairwise-Distance Topic Model as an approach to address the problem of modeling long duration activities with topics. Finally, we consider an application of our work to the study of influencing factors in human opinion change with mobile sensor data. We consider the Social Evolution Project Reality Mining dataset, and investigate other mobile phone sensor features including communication logs. We consider the difference in behaviors of individuals who change political opinion and those who do not. We combine several types of data to form multimodal exposure features, which express the exposure of individuals to others' political opinions. We use the previously defined methodology based on Latent Dirichlet Allocation to define each group's behaviors in terms of their exposure to opinions, and determine statistically significant features which differentiate those who change opinions and those who do not. We also consider the difference in exposure features of individuals that increases their interest in politics versus those who do not. Overall, this thesis addresses several important issues in the recent body of work called Computational Social Science. Investigations principled on mathematical models and multiple types of mobile phone sensor data are performed to mine real life human activities in large-scale scenarios.

EPFL2011

A Probabilistic Approach to Socio-Geographic Reality Mining

Katayoun Farrahi

EPFL2011

Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models

Katayoun Farrahi, Daniel Gatica-Perez

2011