4. i
How many will we get?
~22 likes
per day*
* http://popularity.csail.mit.edu/
5. What makes an image popular?
2.3M images from Flickr with
#views grouped by users
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]
8. Social network?
#
#
#
#
#
#
mean views
of the user
images
uploaded
group
memberships
contacts
tags
title length
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]
18. Early patterns reflect long-term interest
[Predicting the popularity of online content. G. Szabo and B. A. Huberman, ACM’10]
19. Prediction ~ Regression
Univariate Linear (UL)
Multivariate Linear (ML)
ML + Radial Basis Function
Support Vector Regression
[Using early patterns to predict the popularity of YouTube videos. H. Pinto et al., WSDM’13]
[Predicting popularity of online videos using Support Vector Regression. T. Trzcinski and P. Rokita, sub. TCSVT’15]
Φ(x, y) = exp
✓
−
||x − y||2
2σ2
◆
∀
t<T
viewsUL(v, T) ∼ ln (views(v, t))
∀
t1,...,tn<T
viewsML(v, T) ∼ ln (views(v, t1), ..., views(v, tn))
viewsSV R(v, T) ∼ Φ (views(v), ..., views(SV ))
viewsRBF (v, T) ∼ viewsML + Φ (views(v), ..., views(random))
20. Prediction before publication
Visual cues only
Video length
Dominant color
Scene dynamics
Text (OCR)
Faces
Clutter
Thumbnails
[Predicting popularity of online videos using Support Vector Regression. T. Trzcinski and P. Rokita, sub. TCSVT’15]