Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2009

Abstract

User generated content (UGC), now with millions of video producers and consumers, is re-shaping 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

Interactive TV, human factors, exponential distributions, log normal distributions, pareto distributions, probability, copyright protection

Discipline

Databases and Information Systems | Hardware Systems

Research Areas

Data Science and Engineering

Publication

IEEE/ACM Transactions on Networking

Volume

17

Issue

5

First Page

1357

Last Page

1370

ISSN

1063-6692

Identifier

10.1109/TNET.2008.2011358

Publisher

Institute of Electrical and Electronics Engineers (IEEE) / Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1109/TNET.2008.2011358

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