DocumentCode :
1193071
Title :
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
Author :
Cha, Meeyoung ; Kwak, Haewoon ; Rodriguez, Pablo ; Ahn, Yong-Yeol ; Moon, Sue
Author_Institution :
Networked Syst. Res. Group, Max Planck Inst. for Software Syst. (MPI-SWS), Saarbrucken, Germany
Volume :
17
Issue :
5
fYear :
2009
Firstpage :
1357
Lastpage :
1370
Abstract :
User generated content (UGC), now with millions of video producers and consumers, is reshaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world\´s largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show how UGC services are fundamentally different from traditional VoD services. We then analyze the intrinsic statistical properties of UGC popularity distributions and discuss opportunities to leverage the latent demand for niche videos (or the so-called "the Long Tail" potential), which is not reached today due to information filtering or other system scarcity distortions. Based on traces collected across multiple days, we study the popularity lifetime of UGC videos and the relationship between requests and video age. Finally, we measure the level of content aliasing and illegal content in the system and show the problems aliasing creates in ranking the video popularity accurately. The results presented in this paper are crucial to understanding UGC VoD systems and may have major commercial and technical implications for site administrators and content owners.
Keywords :
Pareto distribution; log normal distribution; telecommunication services; video on demand; Daum Videos; VoD; YouTube; content aliasing; the Long Tail potential; user generated content; video popularity; video-on-demand; Interactive TV; copyright protection; exponential distributions; human factors; log normal distributions; pareto distributions; probability;
fLanguage :
English
Journal_Title :
Networking, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1063-6692
Type :
jour
DOI :
10.1109/TNET.2008.2011358
Filename :
4801529
Link To Document :
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