rise and fall patterns of information diffusion: model and implications yasuko matsubara (kyoto...
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Rise and Fall Patterns of Information Diffusion:Model and ImplicationsYasuko Matsubara (Kyoto University), Yasushi Sakurai (NTT), B. Aditya Prakash (CMU), Lei Li (UCB), Christos Faloutsos (CMU)
KDD 2012 1Y. Matsubara et al.
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Motivation
Social media facilitate faster diffusion of news and rumors
KDD 2012 2
Q: How do news and rumors spread in social media?
Y. Matsubara et al.
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News spread in social media
MemeTracker [Leskovec et al. KDD’09] short phrases sourced from U.S. politics in 2008
KDD 2012 3
“you can put lipstick on a pig” (# of mentions in blogs)
“yes we can”
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(per hour, 1 week)
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News spread in social media
MemeTracker [Leskovec et al. KDD’09] short phrases sourced from U.S. politics in 2008
KDD 2012 4
“you can put lipstick on a pig” (# of mentions in blogs)
“yes we can”
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Breaking news
Decay News
spread
(per hour, 1 week)
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Rise and fall patterns in social media
Twitter (# of hashtags per hour)
Google trend (# of queries per week)
KDD 2012 5Y. Matsubara et al.
“#assange” “#stevejobs”
“harry potter” (2010 - 2011) “tsunami” (in 2005)
(per hour, 1week) (per hour, 1 week)
(per week, 1 year) (per week, 2 years)
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Rise and fall patterns in social media
KDD 2012 6
How many patterns are there? -Earlier work claims there’re several classes• four classes on YouTube [Crane et al.
PNAS’08]• six classes on Media [Yang et al.
WSDM’11]
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Rise and fall patterns in social media
KDD 2012 7
Q. How many classes are there after all?
A. Our answer is “ONE”!
We can represent all patterns by single model
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Outline
KDD 2012 8
- Motivation- Problem definition- Proposed method- Experiments- Discussion – SpikeM at work- Conclusions
Y. Matsubara et al.
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Problem definition
KDD 2012 9
Goal: predict/model social activity
Given:
- Network of bloggers/users
- External shock/event- Quality of the event βFind:
- How blogging activity will evolve over time
Problem 1 (What-if?)β
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Problem definition
KDD 2012 10
Goal: predict/model social activity
Given:
- Behavior of spikesFind:
- Equation/model that can explain them, e.g.,
- # of potential bloggers- Strength of external
shock- Quality of the event β
Epidemic process by word-of-mouth
Problem 2 (Model design)
β
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Outline
KDD 2012 11
- Motivation- Problem definition- Proposed method- Experiments- Discussion – SpikeM at work- Conclusions
Y. Matsubara et al.
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Proposed method
SpikeM capture 3 properties of real spike
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1.
periodicities
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Proposed method
SpikeM capture 3 properties of real spike
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2. avoid infinity
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1.
periodicities
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Proposed method
SpikeM capture 3 properties of real spike
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3. power-law fall
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1.
periodicities2. avoid infinity
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Proposed method
SpikeM capture 3 properties of real spike
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3. power-law fall
SpikeM capture behavior of real spikes
using few parameters Y. Matsubara et al.
1.
periodicities2. avoid infinity
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Main idea (details)- 1. Un-informed bloggers (clique of N
bloggers/nodes)
KDD 2012 16
Time n=0
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Nodes (bloggers) consist of two states
- Un-informed of rumor
- informed, and Blogged about
rumor
U
B
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Main idea (details)- 1. Un-informed bloggers (clique of N
bloggers/nodes)- 2. External shock at time nb (e.g, breaking
news)
KDD 2012 17
Time n=0 Time n=nb
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External shock- Event happened at time - bloggers are informed, blog about
news
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Main idea (details)- 1. Un-informed bloggers (clique of N
bloggers/nodes)- 2. External shock at time nb (e.g, breaking
news)- 3. Infection (word-of-mouth effects)
KDD 2012 18
Time n=0 Time n=nb Time n=nb+1
β
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Infectiveness of a blog-post- Strength of infection (quality of news)
- Decay function (how infective a blog posting is)
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Main idea (details)- 1. Un-informed bloggers (clique of N
bloggers/nodes)- 2. External shock at time nb (e.g, breaking
news)- 3. Infection (word-of-mouth effects)
KDD 2012 19
Time n=0 Time n=nb Time n=nb+1
β
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Infectiveness of a blog-post- Strength of infection (quality of news)
- Decay function (how infective a blog posting is)
Decay function:
Linear scale Log scale
-1.5
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SpikeM-base (details)
Equations of SpikeM (base)
KDD 2012 20
- Total population of available bloggers
- Strength of infection/news- External shock at birth (time )- Background noise
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Un-informed
Blogged
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SpikeM - with periodicity (details)Full equation of SpikeM
KDD 2012 21Y. Matsubara et al.
Un-informed
Blogged Periodicity
12pmPeak activity 3am
Low activity
Time n
activity
Bloggers change their activity over
time(e.g., daily, weekly, yearly)
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Learning parameters- Given a real time sequence
- Minimize the error (Levenberg-Marquardt (LM) fitting)
Model fitting (Details)
SpikeM consists of 7 parameters
KDD 2012 22Y. Matsubara et al.
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Analysis
SpikeM matches realityexponential rise and power-raw fall
KDD 2012 23
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rise fall
SpikeM vs. SI model (susceptible infected model)
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Analysis
KDD 2012 24
Linear-log
Log-log
Rise-part
SpikeM: exponential SI model: exponential
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rise fall
Reverse
x-axis
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Analysis
KDD 2012 25
Linear-log
Log-log
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rise fall
Fall-part SpikeM: power law SI model:exponential
SpikeM matches reality
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Outline
KDD 2012 26
- Motivation- Problem definition- Proposed method- Experiments- Discussion – SpikeM at work- Conclusions
Y. Matsubara et al.
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Experiments
We answer the following questions…
KDD 2012 27
Q1. Match real spikes- Q1-1: K-SC clusters- Q1-2: MemeTracker- Q1-3: Twitter - Q1-4: Google trend
Q2. Forecast future patterns
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Q1-1 Explaining K-SC clusters
KDD 2012 28
Six patterns of K-SC [Yang et al. WSDM’11]
SpikeM can generate all patterns in K-SC Y. Matsubara et al.
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Q1-2 Matching MemeTracker patterns
KDD 2012 29
MemeTracker (memes in blogs) [Leskovec et al. KDD’09]
SpikeM can fit various patterns in blog
Linear scale
Log scale
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Noise-robust fitting
Outliers
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Q1-3 Matching Twitter data
KDD 2012 30
Twitter data (hashtags)
SpikeM can generate various patterns in social media
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Linear scale
Log scale
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Q1-4 Matching Google trend data
KDD 2012 31
Volume of searches for queries on Google
SpikeM can capture various patternsY. Matsubara et al.
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Q2 Tail-part forecasts
KDD 2012 32
- Given a first part of the spike - forecast the tail part
SpikeM can capture tail part (AR: fail)
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Outline
KDD 2012 33
- Motivation- Problem definition- Proposed method- Experiments- Discussion – SpikeM at work- Conclusions
Y. Matsubara et al.
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SpikeM at work
SpikeM is capable of various applications
KDD 2012 34
- A1. What-if
forecasting- A2. Outlier detection- A3. Reverse
engineering
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A1. “What-if” forecasting
KDD 2012 35
Forecast not only tail-part, but also rise-part!
e.g., given (1) first spike,(2) release date of two sequel movies (3) access volume before the release date
? ?
(1) First spike (2) Release date
(3) Two weeks before release
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A1. “What-if” forecasting
KDD 2012 36
Forecast not only tail-part, but also rise-part!
SpikeM can forecast upcoming spikes Y. Matsubara et al.
(1) First spike (2) Release date
(3) Two weeks before release
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A2. Outlier detection
KDD 2012 37
Fitting result of “tsunami (Google trend)” in log-log scale
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Another earthquake
One year after Indian
Ocean earthquake
Indian Ocean
earthquake
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A3. Reverse engineering
KDD 2012 38
SpikeM provide an intuitive explanationPDF of parameters over 1,000 memes/hashtags
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Meme
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A3. Reverse engineering
KDD 2012 39
SpikeM provide an intuitive explanationPDF of parameters over 1,000 memes/hashtags
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Observation 1
Total population N is almost sameMeme
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A3. Reverse engineering
KDD 2012 40
SpikeM provide an intuitive explanationPDF of parameters over 1,000 memes/hashtags
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Observation 2
Strength of first burst
(news) is
Meme
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A3. Reverse engineering
KDD 2012 41
SpikeM provide an intuitive explanationPDF of parameters over 1,000 memes/hashtags
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Observation 3
Daily periodicity with phase shift
Every meme has the same periodicity without
lag
(Twitter)Daily periodicity with
more spread in(i.e., Multiple time zone)
Meme
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Outline
KDD 2012 42
- Motivation- Background- Proposed method- Experiments- Discussion – SpikeM at work- Conclusions
Y. Matsubara et al.
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Conclusions
KDD 2012 43
SpikeM has following advantages:
• Unification powerIt includes earlier patterns/models
• Practicality: It Matches real datasets
• ParsimonyIt requires only 7 parameters
• Usefulness: What-if scenarios, outliers, etc.
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Acknowledgements
Thanks Jaewon Yang & Jure Leskovec for the six clusters
[WSDM’11]
Funding
44KDD 2012 Y. Matsubara et al.
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Thank you
KDD 2012 45Y. Matsubara et al.
Code: http://www.kecl.ntt.co.jp/csl/sirg/people/yasuko/software.html
Email: matsubara.yasuko lab.ntt.co.jp
Yasushi Sakurai
Yasuko Matsubara
B. Aditya Prakash
Lei Li Christos Faloutsos