Predicting YouTube content popularity via Facebook data: A network spread model for optimizing multimedia delivery
Dinuka A. Soysa, Denis Guangyin Chen, Oscar C. Au, Amine Bermak · 2013
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.