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
Who Are Your Friends?
Who do you discuss Important Matters with?
Who do you spend your Free Time with?
Connected
One Pair
Connected
Connected
Many Pairs
Connected
Interconnected
Connected
Social Network
The Power of FriendsConnected
Friends as DataFriends as MotivatorsFriends as Multipliers
Friends as Sensors
The Framingham Heart Study
Connected
Original Cohort1948N = 5,209
Offspring Cohort1971N = 5,124
Gen 3 Cohort2002N ~ 4,000
ObesityClusters
FHS NETWORK
Connected
Three DegreesOf Association
FHS NETWORK
Connected
HOMOPHILY
Causes of Similarity and Clustering
INFLUENCE CONTEXT
Connected
The SpreadOf Obesity
Connected
FROM 1971 TO 2003
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
20011971
Smoking ClustersFHS NETWORK
Connected
Drinking ClustersFHS NETWORK
Connected
ANGER HAPPINESS
Reading Emotions
Connected
HappinessClusters
FHS NETWORK
Connected
GenerosityCascades
EXPERIMENTALNETWORK
Connected
How Do We Take Our Natural Social Networks Online?
Connected
Connected
OnlineNetworks
Connected
FULL NETWORK
OnlineNetworks
Connected
NO EFFECT!
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
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
PhotoTagging
Connected
SmilingClusters
FACEBOOK NETWORK
Smilers
NonSmilers
Connected
ObesityClusters
FACEBOOK NETWORK
Connected
ViralVoting
FACEBOOK NETWORK
Connected
ViralVoting
FACEBOOK NETWORK
Connected
ViralVoting
FACEBOOK NETWORK
Connected
Connected
Connected
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
Connected
Connected
Network
Connected
Theoretical Differences in Epidemic CurvesC
UM
ULA
TIV
E IN
CID
EN
CE
OF
CO
NTA
GIO
N
DA
ILY
INC
IDE
NC
EO
F C
ON
TAG
ION
TIME TIME
Connected
PopulationRANDOM PEOPLE
Connected
PopulationPEOPLE & FRIENDS
Connected
Observed Differences in Epidemic CurvesC
UM
ULA
TIV
E IN
CID
EN
CE
OF
INF
LUE
NZ
A
DA
ILY
INC
IDE
NC
EO
F IN
FLU
EN
ZA
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
Connected
PATHOGENS INFORMATION NORMS BEHAVIORS
Contagious Outbreaks
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
Connected
Sensor vs. control – specific examples
Connected
Global view of lead times
Connected
More Active AND More Diverse
ConnectedConnected
Early Warning, Even Out of Sample
PowerfulFriends
Connected
Friends as DataFriends as MotivatorsFriends as MultipliersFriends as Sensors
Connected
Connected
Connected
Realize Your Own Network Power
Connected
Thank You!