SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Shamma, D.A., Yew, J., Kennedy, L. and Churchill, E.F. (2011) Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors. ICWSM.

has been cited by the following article:

  • 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.