The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
Open accessAbstract
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.
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Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
Classes of small-world networks
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200327197 · Cited by 3,051
The long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869
Self-similarity in World Wide Web traffic: evidence and possible causes
Crovella, M. E., & Bestavros, A. (1997) · IEEE/ACM Transactions on Networking · doi:10.1109/90.650143 · Cited by 2,731
Log-normal distributions across the sciences: keys and clues
LIMPERT, E., STAHEL, W. A., & ABBT, M. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2 · Cited by 2,621
II.—A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F. R. S
Yule, G. U. (1925) · Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character · doi:10.1098/rstb.1925.0002 · Cited by 1,718
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298309 · Cited by 1,576
Self-citationA Brief History of Generative Models for Power Law and Lognormal Distributions
Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129088 · Cited by 1,188
Youtube traffic characterization: a view from the edge
Gill, P., Arlitt, M., Li, Z., & Mahanti, A. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298310 · Cited by 852
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Wang, S., Zhang, X., Zhang, Y., Wang, L., YANG, J., & Wang, W. (2017) · IEEE Access · doi:10.1109/access.2017.2685434 · Cited by 952
From Scaling Disparities to Integrated Parallelism: A Decathlon for a Decade
Winzer, P. J., & Neilson, D. T. (2017) · Journal of Lightwave Technology · doi:10.1109/jlt.2017.2662082 · Cited by 470
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Tan, L. T., & Hu, R. Q. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2867191 · Cited by 356
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
Commenting on YouTube videos: From guatemalan rock to El Big Bang
Thelwall, M., Sud, P., & Vis, F. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21679 · Cited by 206
Metadata
- Authors
- Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S.
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 2009-10
- Volume
- 17
- Issue
- 5
- Pages
- 1357-1370
- ISSN
- 1063-6692 (ISSN-L)
- 1558-2566
- OpenAlex
- W2154851929
- Cited by
- 485
- References
- 20
- FWCI
- 26.13
- Type
- journal article
- Language
- en
- Open access
- green
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