carnegie mellon school of computer science modeling website popularity competition in the...
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Carnegie MellonSchool of Computer Science
Modeling Website Popularity Competition in the Attention-Activity
Marketplace
Bruno Ribeiro Christos FaloutsosCarnegie Mellon University
WSDM 2015February 5, 2015
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Motivation
Bought for $580 million
in 2005
Sold for $35 million in
2011
*source: TechCrunch
MySpace’s demise
Sold
Hi5Friendster
Summer 2008
Summer 2008
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Predicting Network Popularity w/
Competition
Goal:
Why:
??
?
Summer 2008
Fract
ion o
f A
ctiv
e U
sers
(t)
-$545 million dollars
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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new adopters/semester
MySpace.com until late 2008
No Structural Telltale Signs of MySpace’s Demise
Matches known theoretical behaviorMansfield’61, Rogers’03, Bass’69
Total Adopters
0Innovators2.5 %
EarlyAdopters13.5 %
EarlyMajority34 %
LateMajority34 %
Laggards16 %
And no abrupt topological changes in 2008
MySpace graph until recently:• 35 million users
★ http://www.myspace.com
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Economics:◦ (Mansfield ’63)◦ (Katz&Shapiro’85) ◦ (Farrell&Saloner
’86)◦ (Choi ’94)◦ (Arthur ’94)
Marketing:◦ (Bass ’69)◦ (Fisher&Pry ’71)
Fract
ion o
f A
ctiv
e U
sers
(t)
Summer 2008
Background: Vast Adoption Literature
Computer Science: (Kempe et al ’03) (Zhao et al., IMC’12) (Leskovec et al.,
SIGKDD’08) (Ugander et al., PNAS’12) (Aral&Walker,
Science’12)
Sociology:o (Ryan&Gross’49)o (Everett ’62, ’03)o (Rogers ’03)o (Centola ’12)
Adopt ≠ Active
We know how to model adoption
But how to model attention?
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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1969 - H. A. Simon on information overload:
Information consumes attention (time)
◦ Information-rich world Attention-poor world
Systems that “talk” more than “think” exacerbate
information overload
Attention & Information Overload
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
Facebookattention& activity
7
How Facebook Talks (a lot)
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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My Attentio
n
My Content
Friends’ Attentio
n
Friends’ Content
Positive & Negative Attention Loops
Positive
Growth
Negative Growth
WebsiteSurvives
WebsiteDies
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Proposed Competition Model
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Proposed State Space
states ∅yUy Ay Iy
∅x
UxAxIx
Both x & y Only y for now Only x for now
Neither for now Not interested in x/y
User Subscribes to:
WebsiteKillers
Never! Unaware Active Inactive
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Model Transitions
User Competition
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Isolated Network Transitions
states ∅yUy Ay Iy SUM
∅x
Ux S(U, )∅ (t)
Ax S(A, )∅ (t) S(A,*)(t)
Ix S(I, )∅ (t)
Media/Marketing gets new subscribers Word of mouth gets subscribers
Network x activity attracts user back
Other activitytakes user away from x
Details
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Model Predictions with Constant Distraction Factor
Predicting Online Social
Network Popularity
◦ How websites do on
their own
◦ Evolution with
competition
Outline
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Signatures of Self-Sustainability
Long-term User Activity[Ribeiro’14]
Relative attractiveness of activity
Fract
ion
of
Act
ive U
sers
(t)
Fract
ion
of
Act
ive U
sers
(t)
t t
≅1MySpace
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Signatures of Popularity Growth
Model prediction: Signatures of activity growth[Ribeiro’14]
t tFract
ion
of
Act
ive U
sers
(t)
Fract
ion
of
Act
ive U
sers
(t)
Wor
d of
mou
th
Med
ia &
Mar
ketin
g
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Predictions with Changing Distractions
t
Predicting Online Social
Network Popularity
◦ How websites do on
their own
◦ Evolution with
competition
Outline
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Two observable states: S(A,*)(t) and S(*,A)(t)
Remaining states latent & treated as parameters
Data from Alexa.com
Fitted with Levenberg–Marquardt algorithm using squared error
Model Fitting
S(A,*)(t)
S(I,*)(t)?S(U,*)(t)?S(A,I)(t)?S(A,A)(t)?S(A,U)(t)?S(A, )∅ (t)?S(I, )∅ (t)?S(U, )∅ (t)?
friendster.com
Fract
ion
of
Act
ive U
sers
(t)
Details
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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MySpace
Friendster
Multiply
Hi5
Facebook Introduces Wall
Distraction of Concurrent UsersIncreases as Facebook Introduces
Wall
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Results (Facebook x MySpace)
t
value
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Results (Facebook x Multiply)
t
value
Multiply
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Results (Facebook x Hi5)
t
value
Hi5
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Results (Facebook x Hi5)
Model Captures Coexistence and Then
Death of Facebook Competitors
Too Many Concurrent Users = Fragility
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Attention feedback helps predict /
understand network survival
No marketing can save from negative
attention loop
Insights from model
◦ E.g.: Metrics of node centrality should take
attention feedback into account
Conclusions
Bruno Ribeiro ([email protected])Carnegie MellonSchool of Computer Science
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Thank you!
Questions?
http://www.cs.cmu.edu/~ribeiro@brunofmr