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Predicting YouTube Content Popularity via
Facebook Data: A Network Spread Model for
Optimizing Multimedia Delivery
Speaker : Yu-Hui Chen
Authors : Dinuka A. Soysa, Denis Guangyin Chen, Oscar C.
Au, and Amine Bermak
From : 2013 IEEE Symposium on Computational
Intelligence and Data Mining (CIDM)
outline
1.
2.
3.
4.
5.
Introduction
Methodology
Simulation results
Future work
Conclusion
1.Introduction
 Through websites such as Facebook and YouTube to share
multimedia content, the limited network resources, access to
large amounts of multimedia data is a major challenge.
 This paper proposes a Fast Threshold Spread Model (FTSM)
to predict the future access pattern of multi-media content
based on the social information of its past viewers.
2.Methodology
An example infection process of
Independent Cascade Model
A) Facebook Data Mining
Experimental setup: Requesting, downloading and analyzing
JSON objects from Facebook
B) YouTube Video Statistics Mining
 The YouTube statistics provided by YouTube API
C) Fast Threshold Spread Model
G=(V,E)
W(m)=0.5A1(m)+0.5A2(m)
D) Complexity Analysis on a Small
Network vs a Large Network
3.Simulation results
A) Determining Global Threshold
 Effect on NumActiveNodes by changing the Threshold
B) Power Law behavior of the Facebook
Dataset
 Plot of Node Degree vs Number of Nodes in linear scale
B) Power Law behavior of the Facebook
Dataset
 Plot of Node Degree vs Number of Nodes in log scale
C) Correlation between Facebook social sharing
and YouTube Global hit-count
 Scatter plot of top 10 viral videos’ Global YouTube hit count vs
FTSM predictor’s spread count
D) Transient spread simulation
compared with YouTube data
 Normalized view count for FTSM simulation (in red) and YouTube data
(in blue) for top 9 viral videos in the Facebook Dataset
4.Future work
 FTSM for a large network of a few million nodes results in
very long execution time.
 This paper is able to show that a small network’s.
 A large network can be partitioned into multiple small
networks .(ex. Hong Kong)
5.Conclusion
 The Fast Threshold Spread Model (FTSM) was used to
perform fast prediction of multi-media content propagation
based on the social information of its past viewers.
 This can be a solution to the cache management challenges
when prioritizing.