Reality Mining: Sensing Complex Social Systems Nathan Eagle and Alex (Sandy) Pentland MIT Media Laboratory

1
Reality Mining Sensing Complex Social
SystemsNathan Eagle and Alex (Sandy)
PentlandMIT Media Laboratory

• Presented by Kyle Dorman
• May 12, 2008

2
Motivation

• Current surveys are limited
• Even surveys that utilize location-aware devices
  could not scale to large groups
• Mobile phones can be used to study individuals
  and organizations
• Goal Create a predictive classifier that can
  perceive aspects of a users life more accurately
  than a human observer

3
Bluetooth

• Bluetooth MAC address (BTID)
• Hex number unique to the particular device
• Device name
• Set by user
• Device type
• 3 integers that correspond to the device
  discovered

4
BlueAware

• Software application to log BTIDs
• Records and timestamps BTIDs encountered in a
  proximity log
• Runs in the background

Fig 1. BlueAware running in the foreground on a
Nokia Series 60 phone
5
Dataset

• One Hundred Nokia 6600 smart phones
• 75 students or faculty in the MIT Media
  Laboratory
• 25 incoming students at MIT Sloan business school
  adjacent to the lab
• Nine month study (500,000 hours)

6
User Modeling

• Identifiable routines
• Daily routines, weekly patterns, yearly patterns
• Model three states home, work and elsewhere
• Data obtained from Bluetooth, cell tower and
  temporal information

7
Location

• Detecting by cell towers is complicated
• Within range of over a dozen cell towers
• More than a couple miles away from a tower
• Detecting with GPS works well outside
• Inside issue is with line-of-sight
• Incorporated static Bluetooth device ID
• Such as a desktop computer

8
Location Bluetooth
Fig 3. The number of Bluetooth encounters for
Subject 9 over the month of January

• 10 most frequently detected Bluetooth devices for
  one subject
• Provides insight into the times the user is in
  his office
• Also, insight into his relationship with other
  subjects
• Leaves his office at 1400 and becomes
  consistently near to subject 4
• Strong cutoffs at 900 and 1700 show that
  subject has low entropy

9
Results Low Entropy Subject
Fig 4. Subject 9's 'low entropy' daily
distribution of home/work transitions and
Bluetooth devices. The 'hot spot' in mid-day is
when the subject is at the workplace.
10
Results High Entropy Subject
Fig 5. Subject 4's 'high entropy' daily
distribution of home/work transitions
and Bluetooth devices.
11
Cell Phone Applications
Fig 7. Average application usage of 100 subjects
with location
12
Possible Errors

• Data Corruption
• Data was stored on flash memory card
• Because of finite number of read-write cycles, a
  new card failed after a month
• Changed to store incremental logs in RAM and
  write complete logs to flash memory
• Bluetooth
• Can penetrate some types of walls

13
Human Induced Errors

• Phone turned off
• Phone separated from user
• Forgotten phone classifier
• Staying in same location for a period of time
• Remaining idle
• Classifier did identify days correctly but also
  had false positives
• Missing Data data corruption powered off
  devices
• Positive Users filled out surveys about
  activities and people they interact with
  throughout the day
• Found correlation between survey and proximity
  data

14
Another application

• Logging and time-stamping users activity,
  location and proximity to other users
• People who users only see in a specific context
• By knowing activity, time of day and even
  location, can calculate probability of user
  seeing a specific individual using Bayes rule
• Predictions can inform user of the most likely
  time and place to find specific colleagues

15
Human Landmarks
Fig 11. Plotted is proximity frequency data for a
friend and a workplace acquaintance of one
subject.
16
Relationship Inference
Fig 10. Friendship (left) and daily proximity
(right) networks share similar structure. Circles
represent incoming Sloan business school
students. Triangles, diamonds and squares
represent senior students, incoming students, and
faculty/staff/freshman at the Media Lab.
17
Organizational Rhythms

• Dynamics of behavior in organization in response
  to external and internal stimuli
• Example In October, 75 Media Lab subjects were
  working towards annual visit of sponsors
• Significant fraction spent much of the night in
  the lab just before the event

18
Conclusion

• Applications studied
• Individual behavior modeling
• Device usage
• Relationship inference
• Group behavior analysis
• Many more applications this kind of data can be
  applied to

19
Questions?