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MIT Media Lab: Reality Mining

Machine Perception and Learning of Complex Social Systems

Reality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.

The original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.

We have captured communication, proximity, location, and activity information from 100 subjects at MIT over the course of the 2004-2005 academic year. This data represents over 350,000 hours (~40 years) of continuous data on human behavior. Such rich data on complex social systems have implications for a variety of fields. The research questions we are addressing include:

  • How do social networks evolve over time?
  • How entropic (predictable) are most people's lives?
  • How does information flow?
  • Can the topology of a social network be inferred from only proximity data?
  • How can we change a group's interactions to promote better functioning?

We have cleaned the 2004-2005 data of identifiable information and are making it available to other researchers within the academic community. Both the mobile phone application and the resultant dataset can be downloaded here.

This project was generously supported by our partners and collaborators at Nokia.




© 2003-2013 Nathan Eagle / MIT