james fowler: the power of friends: business applications of network science
DESCRIPTION
James H. Fowler earned a PhD from Harvard in 2003 and is currently Professor of Medical Genetics and Political Science at the University of California, San Diego. His work lies at the intersection of the natural and social sciences, with a focus on social networks, behavior, evolution, politics, genetics, and big data. James was was named one of Foreign Policy’s Top 100 Global Thinkers, TechCrunch’s Top 20 Most Innovative People in Democracy, and Most Original Thinker of the year by The McLaughlin Group. Together with Nicholas Christakis, James wrote a book on social networks for a general audience called Connected. Connected has been translated into twenty languages, named an Editor’s Choice by the New York Times Book Review, and featured in Wired, Oprah’s Reading Guide, Business Week’s Best Books of the Year, and a cover story in New York Times Magazine. We were delighted to host James in skatepark Waalhalla for #projectwaalhalla Social Science for Startups. See our meet up group for more events: http://www.meetup.com/Project-Waalhalla-Social-science-for-StartupsTRANSCRIPT
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Who Are Your Friends?
Who do you discuss Important Matters with?
Who do you spend your Free Time with?
Connected
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One Pair
Connected
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Connected
Many Pairs
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Connected
Interconnected
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Connected
Social Network
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The Power of FriendsConnected
Friends as DataFriends as MotivatorsFriends as Multipliers
Friends as Sensors
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The Framingham Heart Study
Connected
Original Cohort1948N = 5,209
Offspring Cohort1971N = 5,124
Gen 3 Cohort2002N ~ 4,000
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ObesityClusters
FHS NETWORK
Connected
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Three DegreesOf Association
FHS NETWORK
Connected
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HOMOPHILY
Causes of Similarity and Clustering
INFLUENCE CONTEXT
Connected
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The SpreadOf Obesity
Connected
FROM 1971 TO 2003
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Spread of Obesity
Connected
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
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20011971
Smoking ClustersFHS NETWORK
Connected
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Drinking ClustersFHS NETWORK
Connected
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ANGER HAPPINESS
Reading Emotions
Connected
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HappinessClusters
FHS NETWORK
Connected
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GenerosityCascades
EXPERIMENTALNETWORK
Connected
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How Do We Take Our Natural Social Networks Online?
Connected
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Connected
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OnlineNetworks
Connected
FULL NETWORK
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OnlineNetworks
Connected
NO EFFECT!
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OnlineNetworks
REAL FRIENDS
Connected
K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,” Social Networks 2008; 30: 330-342
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OnlineNetworks
REAL FRIENDS
Connected
PLUS FACEBOOK
K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,” Social Networks 2008; 30: 330-342
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PhotoTagging
Connected
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SmilingClusters
FACEBOOK NETWORK
Smilers
NonSmilers
Connected
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ObesityClusters
FACEBOOK NETWORK
Connected
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ViralVoting
FACEBOOK NETWORK
Connected
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ViralVoting
FACEBOOK NETWORK
Connected
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ViralVoting
FACEBOOK NETWORK
Connected
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Connected
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Connected
Initially targeted High influence & high receptivenessSecond wave More receptiveThird wave Increasing acceptance
Measuring susceptibility--Intervention is more effective
Express Scripts
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Email Data at Healthways-Each node represents an employee -Each line represents >100 emails transferred between nodes
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BMI Ranks and Obesity at HealthwaysRed lines show bi-directional ties
Grey lines are directed ties
Body Mass Index (BMI) > 30is considered obese
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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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Connected
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Connected
Network
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Connected
Theoretical Differences in Epidemic CurvesC
UM
ULA
TIV
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CID
EN
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OF
CO
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GIO
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INC
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TAG
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TIME TIME
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Connected
PopulationRANDOM PEOPLE
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Connected
PopulationPEOPLE & FRIENDS
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Connected
Observed Differences in Epidemic CurvesC
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TIV
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INF
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NZ
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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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Connected
PATHOGENS INFORMATION NORMS BEHAVIORS
Contagious Outbreaks
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Connected
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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Connected
Sensor vs. control – specific examples
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Connected
Global view of lead times
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Connected
More Active AND More Diverse
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ConnectedConnected
Early Warning, Even Out of Sample
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PowerfulFriends
Connected
Friends as DataFriends as MotivatorsFriends as MultipliersFriends as Sensors
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Connected
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Connected
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Connected
Realize Your Own Network Power
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Connected
Thank You!