Quantify and Examined of video distribution sites for appreciative links

Devaki Sravani, Anantha Rao .G

Abstract


Videos are an integral part of current information technologies and the snare. The demand for efficient retrieval rises with the increasing number of videos, which is equally true for video annotation techniques as matadata is the primary source of most retrieval systems. - Recently, many video distribution sites provide external links so that their video or audio contents can be embedded into external snare sites. Online social networks (OSNs) have become popular desti- nations for connecting friends and distribution information. Recent statistics suggest that OSN users regularly share con-tents from video sites, and a significant amount of requests of the video sites are indeed from them nowadays. These behaviors have substantially changed the workload of on line video services.In this paper, we provide a comprehensive quantify study and examined source on these external links to answer these two questions. With the traces together from two major video distribution sites, YouTube and Youku of China, we show that the external links have various impacts on the popularity of the video distribution sites.  The social video proposed system provides information that is directed and personalized for the user overriding the content rather than the point of view of the user posting the content. The tradeoff in our case being that users have to sacrifice privacy and have to trust the recommendation system provider with their private data.


References


R. Zhou, S. Khemmarat, and L. Gao, “The impact of youtube recommendation system on video views,” inProc. of IMC 2010, Melbourne, November 1-3 2010.

M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, andS. Moon, “I tube, you tube, everybody tubes:analyzing the world’s largest user generated contentvideo system,” in Proc. of ACM IMC, San Diego,California, USA, October 24-26 2007, pp. 1–14.

R. Crane and D. Sornette, “Viral, quality, and junk videos on YouTube: Separating content from noise in an information-rich environment,” in Proc. of AAAI symposium on Social Information Processing, Menlo Park, California, CA, March 26-28 2008.

P. Gill, M. Arlitt, Z. Li, and A. Mahanti, “YouTube traffic characterization: A view from the edge,” in Proc. of ACM IMC, 2007.

A. Fuxman, P. Tsaparas, K. Achan, and R. Agrawal, “Using the Wisdom of the Crowds for Keyword Generation", In Proc. ACM WWW’08, Beijing, China,April 21 - 25, 2008.

S. Goel, R. Muhamad, and D. Watts, “Social Search in “Small-World”Experiments”, In Proc. ACM WWW’09, Madrid, Spain, April 20 - 24, 2009.

P. , M. Arlittz, Z. Li, and A. Mahanti, “YouTube Traffic Characterization: AView From the Edge”, In Proc. ACM IMC’07, San Diego, California, USA,

October 24-26, 2007.

J. Kunegis, A. Lommatzsch, and C. Bauckhage, “The Slashdot Zoo: Mining aSocial Network with Negative Edges”, In Proc. ACM WWW’09, Madrid, Spain,April 20-24, 2009.

K. Lai and D. Wang “A Measurement Study of External Links of YouTube”, In

Proc. IEEE Globecom’09, Hawaii, USA, November 30 - December 4th, 2009.

K. Lai and D. Wang “The Implication of External links on Video Sharing Sites: Measurement and Analysis”, Technical Report, Department of Computing, TheHong Kong Polytechnic University, Jan, 2010.

Haitao Li, Haiyang Wang, Jiangchuan Liu Video Sharing in Online Social Networks:Measurement and Analysis 2013.


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