Journal of Computer and Communications

Volume 3, Issue 11 (November 2015)

ISSN Print: 2327-5219   ISSN Online: 2327-5227

Google-based Impact Factor: 1.12  Citations  

A Bayesian Approach to Identify Photos Likely to Be More Popular in Social Media

HTML  XML Download Download as PDF (Size: 406KB)  PP. 198-204  
DOI: 10.4236/jcc.2015.311031    3,826 Downloads   4,643 Views  Citations

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.

Share and Cite:

Choudhury, A. and Nagaswamy, S. (2015) A Bayesian Approach to Identify Photos Likely to Be More Popular in Social Media. Journal of Computer and Communications, 3, 198-204. doi: 10.4236/jcc.2015.311031.

Cited by

[1] On the Measurement and Prediction of Web Content Utility: A Review
ACM SIGKDD Explorations Newsletter, 2017

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.