Mobile phones data mining

(via), the reality mining project:

The Reality Mining experiment is one of the largest academic mobile phone projects in the US. 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 are currently capturing communication, proximity, location, and activity information from 100 subjects at MIT over the course of this academic year. To date, we have collected approximately 350,000 hours (~40 years) of continious data on human behavior. Such rich data on complex social systems have implications for a variety of fields. It is our hope that this research will help us explore research questions including:

  • How do incoming students' social networks evolve over time?
  • How entropic (predictable) are most people's lives?
  • 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? If you have a Symbian Series 60 Phone (such as the Nokia 6600) with a data plan, you can participate.

Close to this SmartFriend project 8a tool that compute statistics on your mobile phone about social things like with whom are you spending the largest amount of time on the phone? bet on your next call or gender stats).

They wrote more about their methodology in this paper (Personal and Ubiquitous Computing). Nathan Eagle, one of the researcher in charge of this reality ming project is interviewed in the Feature. Here are relevant excerpts:

Eagle: I primarily look at mobile phone data that can be broken down into three types: location, communication and proximity patterns. We use cell tower IDs to get approximate locations within a few blocks. Communication logs reveal who is calling and texting whom and how often. And Bluetooth scans every five minutes show who is proximate to you.

Eagle: We can do behavior prediction. Depending on the life you lead, I can predict what you're going to do next based on very limited information. Whether it's your morning Starbucks fix or your Saturday afternoon softball game, everyone lives life in routines. One of our algorithms extracts these routine patterns from everyone's daily lives.

Eagle: There has been a lot of work on building more user-centric interfaces. So the kind of data we gather could automatically change the phone functionality according to a certain demographic. For example, Nokia, one of the sponsors of this research, is selling the same phone to soccer moms, power executives and texting teenagers. With just a few days worth of data, we can characterize the user and their usage. Once we do that, we can customize how the phone looks and operates for specific groups of people.

France Telecom is also working on that topic, using neural network to discriminate social patterns. And their "social serendipity" tool is close to Jamie Lawrence's research project.