MIT Media Lab: Reality Mining

Home Visualizations About Publications Contact Us Download

Reality Mining: Complex Social Systems

Relationship Inference

Previously we showed that Bluetooth-enabled mobile phones may be used to discover a great deal about the user's patterns of activity. In this section we will extend this base of user modeling to explore modeling complex social systems.

By continually logging and time-stamping information about a user's activity, location, and proximity to other users, the large-scale dynamics of collective human behavior can be analyzed. If deployed within a group of people working closely together, correlations between the phone log and proximity log could also be used to provide insight behind the factors driving mobile phone use. Furthermore, a dataset providing the proximity patterns and relationships within large groups of people has implications within the computational epidemiology communities, and may help build more accurate models of airborne pathogen dissemination, as well as other more innocuous contagions, such as the flow of information.

Human Landmarks

As shown in this section, there are people who users only see in a specific context (in this instance, at work). If we know the user is at work, information about the time of day, and optionally the location within the building (using static Bluetooth devices) can be used to calculate the probability of that user seeing a specific individual, by the straightforward application of Bayes' rule.

In contrast to previous work that requires access to calendar applications for automatic scheduling [Roth and Unger (2000)], we can generate inferences about whether a person will be seen within the hour, given the user's current context, with accuracies of up to 90% for 'low entropy' subjects. These predictions can inform the user of the most likely time and place to find specific colleagues or friends. We believe that the ability to reliably instigate casual meetings would be of significant value in the workplace. We must also remember, however, that the ability to predict people's movements can be put to less savory uses. Careful consideration must be given to these possibilities before providing free access to such data.

The Relationship Inference Engine

In our User Modeling section, we have discussed how information about location and proximity can be used to infer a user's context. In much the same way, knowledge of the shared context of two users can provide insight into the nature of their association. For example, being near someone at 3pm by the coffee machines confers different meaning than being near them at 11pm at a local bar. However, even simply proximity patterns provide an indication of the structure of the underlying friendship network as shown below. The clique on the top left of each network are the Sloan business students while the Media Lab senior students are at the center of the clique on the bottom right. The first year Media Lab students can be found on the periphery of both graphs.

We have trained a Gaussian mixture model [Duda et al. (2001)] to detect patterns in proximity between users and correlate them with the type of relationship. The labels for this model came from a survey taken by all of the experimental subjects at the end of two months of data collection (some users came late to the study, but were included anyway). The survey asked who they spent time with, both in the workplace and out of the workplace, and who they would consider to be in their circle of friends. We compared these labels with estimated location (using cell tower distribution and static Bluetooth device distribution), proximity (measured from Bluetooth logs), and time of day.

Workplace colleagues, outside friends, and people within a user's circle of friends were identified with over 90% accuracy, calculated over the 2000 potential dyads. Initial examination of the errors indicates that the inclusion of communication logs combined with a more powerful modeling technique, such as Support Vector Machine, will have considerably greater accuracy.

Some of the information that permits inference of friendship is illustrated below. This figure shows that our sensing technique is picking up the commonsense phenomenon that office acquaintances are frequently seen in the workplace, but rarely outside the workplace. Conversely, friends are often seen outside of the workplace, even if they are co-workers. Determining membership in the 'circle of friends' requires cross-referencing between friends: is this person a member of a cluster in the out-of-office proximity data?