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Tracking the Electronic Metadata Trails of Social Networks
| Date post: | 30-Jan-2015 |
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Tracking the Electronic Metadata Trails of Social Networks
Tracking the Electronic Metadata Trails of Social Networks
Luke J. Matthews, Ph.D.Senior Scientific DirectorActivate Networks, [email protected]
SPEAKER
TRACKING TECHNOLOGY: AMAZON RAINFOREST
Tracking the Electronic Metadata Trails of Social Networks
Online data as a proxy for relationships.
TRACKING YOUR SOCIAL LIFE 2.0
Online behavior is a medium for social life
Form, maintain, and dissolve ties
Metadata becomes an embodiment of social life
Social life made manifest
New forms of social consciousness
Tracking the Electronic Metadata Trails of Social Networks
SOCIAL LIFE 2.0
Tracking the Electronic Metadata Trails of Social Networks
SOCIAL LIFE 1.0
Tracking the Electronic Metadata Trails of Social Networks
Matthews et al. 2012, Religion Brain Behavior
SOCIAL LIFE 2.0 AND 1.0 SOMETIMES ARE A LOT ALIKE
Tracking the Electronic Metadata Trails of Social Networks
State et al. 2013, submitted
• Great potential for new modes of social cooperation
• Also potential to mostly reproduce ancient social patterns
WE KNOW WHO IS MAKING YOU FAT FROM YOUR EMAIL LOGS
Tracking the Electronic Metadata Trails of Social Networks
Body Mass Index (BMI)
• BMI is better predicted by email-based network than a survey network.
• Survey network out-predicted only when combined with location, rank, and department data.
• Why? The email network is pregnant with other ‘confounding’ variables relevant to the prediction task.
Healthways Email NetworkNormalized and Filtered
Nodes Colored by Location
Matthews et al. 2013, PLOSONE
NETWORK ROLES
Betweenness Brokers:Lie between others in the network and are key for information flow, or can become bottlenecks.
Top Connectors:Have the largest number of connections to others in the network.
EGOCENTRIC EMAIL – SURVEY COMPARISONS
Tracking the Electronic Metadata Trails of Social Networks
Incoming Ties and Brokering are egocentric network statistics that express an individual’s role within the network.
When the social signal is separated from the noise, we get substantial correlations between egocentric scores from surveys and from email.
Network Statistics Correlated Across Survey and Email Data Inputs
Incoming Ties Brokering
Client 1 0.77 0.55
Client 2 0.74 0.54
Client 3 0.57 0.22
Client 4 0.41 0.16
Client 4 uses a task-tracking robot that shows up just like their own email addresses = noise in the email data not filtered out
Client 3 has less even sampling across working groups = potential noise in the survey not seen in email
NETWORK SILOS
Tracking the Electronic Metadata Trails of Social Networks
The coefficients in the middle column show the increase in probability compared to baseline that two individuals are not connected if they differ for that variable. For example, the survey only analysis indicates individuals in different Departments are 10.1 times more likely to be disconnected.
Variable Contribution to Network SilosSurvey | Email P-value
Department 10.1 | 4.89 <0.001
Role 2.5 | 2.12 <0.001
Location 2.1 | 1.94 <0.001
Legacy Group 1.9 | 1.66 <0.001
Rank 0.88 | 0.98 0.064 | 0.676
Email - Survey Comparisons• Results are qualitatively
and quantitatively similar
• Department is the largest driver of silos
• Rank is not a statistically significant silo driver
YOU AND YOUR 429 CLOSEST FRIENDS
Tracking the Electronic Metadata Trails of Social Networks
Facebook Connect
Tracking the Electronic Metadata Trails of Social Networks
Suggest invitations
User sends invite
New member joins
A FACEBOOK NETWORK
Tracking the Electronic Metadata Trails of Social Networks
RANDOMIZED CONTROLLED TRIAL OF ANI TARGETING
Tracking the Electronic Metadata Trails of Social Networks
Suggest invitations
User sends invite
New member joins
Algorithm generates 71% more invites
Invitees 37% more likely to join
More than doubled the number of new users
YOUR DATA NETWORK LOOKS LIKE THIS
Tracking the Electronic Metadata Trails of Social Networks
ANI mapped this network of over 100,000 people using aggregated information about their current and past addresses, sharing of significant property, and business co-ownership.
The exact same signals occur in this network, but can only be deciphered at scale algorithmically.
Thanks to these people
Meghan CallahanJackie CriscuoloPete DewanJeff Edmonds
Tracking the Electronic Metadata Trails of Social Networks
QUESTIONS?
Ravi KumarCharlotte MaherPeter MatthewsPaul Richard
Elizabeth RulaCharlie Nunn
Wesley Wildman
Luke J. Matthews, Ph.D.Senior Scientific DirectorActivate Networks, [email protected]
Tracking the Electronic Metadata Trails of Social Networks
617.558.0210 | [email protected] | www.activatenetworks.net1 Newton Executive Park, Suite 100 | Newton, MA 02462
