@inproceedings{82ae2bdbe3454a949b0819f16e068772,
title = "Predicting YouTube content popularity via Facebook data: A network spread model for optimizing multimedia delivery",
abstract = "The recent popularity of social networking websites have resulted in a greater usage of internet bandwidth for sharing multimedia content through websites such as Facebook and YouTube. Moving large volumes of multi-media data through limited network resources remains a technical challenge to this day. The current state-of-art solution in optimizing cache server utilization depends heavily on efficient caching policies to determine content priority. 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. The prediction results are compared and evaluated against ground truth statistics of the respective YouTube video. A complexity analysis on the proposed algorithm for large datasets along with the correlation between Facebook social sharing and YouTube global hit count are explored.",
author = "Soysa, \{Dinuka A.\} and Chen, \{Denis Guangyin\} and Au, \{Oscar C.\} and Amine Bermak",
year = "2013",
doi = "10.1109/CIDM.2013.6597239",
language = "English",
isbn = "9781467358958",
series = "Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013",
pages = "214--221",
booktitle = "Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013",
note = "2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 ; Conference date: 16-04-2013 Through 19-04-2013",
}