TITLE:
A Bayesian Approach to Identify Photos Likely to Be More Popular in Social Media
AUTHORS:
Arunabha Choudhury, Sriram Nagaswamy
KEYWORDS:
Bayesian, Supervised Learning, Image Popularity, Classification, Data Mining
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.11,
November
19,
2015
ABSTRACT: With cameras becoming ubiquitous in Smartphones,
it has become a very common trend to capture and share moments with friends and
family in social media. Arguably, the 2 most relevant factors that contribute
to the popularity are: the user’s social aspect and the content of the image
(image quality, objects in the image etc.). In recent years, due to various
security concerns, it has been increasingly difficult to derive social
attributes from social media. Due to this limitation, in this paper we study
what make images popular in social media based on the image content alone. We
use Bayesian learning approach with variable likelihood function in order to
predict image popularity. Our finding shows that a mapping between image
content to image popularity can be achieved with a significant recall and
precision. We then use our model to predict images that are likely to be more
popular from a set of user images which eventually facilitate easy share.