Post on 16-May-2015
description
Who Are Your Friends?
Who do you discuss Important Matters with?
Who do you spend your Free Time with?
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One Pair
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Many Pairs
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Interconnected
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Social Network
The Power of FriendsConnected
Friends as DataFriends as MotivatorsFriends as Multipliers
Friends as Sensors
The Framingham Heart Study
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Original Cohort1948N = 5,209
Offspring Cohort1971N = 5,124
Gen 3 Cohort2002N ~ 4,000
ObesityClusters
FHS NETWORK
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Three DegreesOf Association
FHS NETWORK
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HOMOPHILY
Causes of Similarity and Clustering
INFLUENCE CONTEXT
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The SpreadOf Obesity
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FROM 1971 TO 2003
Spread of Obesity
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NA Christakis and JH Fowler, “The Spread of Obesity in a Large Social Network Over 32 Years,” New England Journal of Medicine 2007; 357: 370-379
Ego-Perceived Friend
Mutual Friend
Alter-Perceived Friend
Same Sex Friend
Opposite Sex Friend
Spouse
Sibling
Same Sex Sibling
Opposite Sex Sibling
Immediate Neighbor
Small Workplace Co-worker
0 100 200 300
PERCENTAGE INCREASE IN RISK OF OBESITY
SOCIAL CONTACT
20011971
Smoking ClustersFHS NETWORK
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Drinking ClustersFHS NETWORK
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ANGER HAPPINESS
Reading Emotions
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HappinessClusters
FHS NETWORK
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GenerosityCascades
EXPERIMENTALNETWORK
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How Do We Take Our Natural Social Networks Online?
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OnlineNetworks
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FULL NETWORK
OnlineNetworks
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NO EFFECT!
OnlineNetworks
REAL FRIENDS
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K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,” Social Networks 2008; 30: 330-342
OnlineNetworks
REAL FRIENDS
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PLUS FACEBOOK
K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,” Social Networks 2008; 30: 330-342
PhotoTagging
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SmilingClusters
FACEBOOK NETWORK
Smilers
NonSmilers
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ObesityClusters
FACEBOOK NETWORK
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ViralVoting
FACEBOOK NETWORK
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ViralVoting
FACEBOOK NETWORK
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ViralVoting
FACEBOOK NETWORK
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Initially targeted High influence & high receptivenessSecond wave More receptiveThird wave Increasing acceptance
Measuring susceptibility--Intervention is more effective
Express Scripts
Email Data at Healthways-Each node represents an employee -Each line represents >100 emails transferred between nodes
BMI Ranks and Obesity at HealthwaysRed lines show bi-directional ties
Grey lines are directed ties
Body Mass Index (BMI) > 30is considered obese
Bikewalk Program in Blue Zones by Healthways-Each node represents an individual-Green nodes are predicted adopters for the Bikewalk program
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Network
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Theoretical Differences in Epidemic CurvesC
UM
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TIME TIME
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PopulationRANDOM PEOPLE
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PopulationPEOPLE & FRIENDS
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Observed Differences in Epidemic CurvesC
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DAYS SINCE SEPTEMBER 1 DAYS SINCE SEPTEMBER 1
0 20 40 60 80 100 120
0.00
0.02
0.42
0.06
0.08
0.00
000.
0004
0.00
080.
0012
0 20 40 60 80 100 120
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PATHOGENS INFORMATION NORMS BEHAVIORS
Contagious Outbreaks
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Twitter Data! 2/3 of the Twittershpere• 476,553,560 tweets• 40,171,624 users• 1,468,365,182 follows
June-December 2009
Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media. Proceedings of the 19th international conference on World wide web, 591–600.
66,935,466 tweets using a hashtag
4,093,624 different hashtags
1,620,896 users using hashtags
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Sensor vs. control – specific examples
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Global view of lead times
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More Active AND More Diverse
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Early Warning, Even Out of Sample
PowerfulFriends
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Friends as DataFriends as MotivatorsFriends as MultipliersFriends as Sensors
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Realize Your Own Network Power
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Thank You!