NDLI: Video sharing in online social networks: measurement and analysis

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Video sharing in online social networks: measurement and analysis

Content Provider	ACM Digital Library
Author	Wang, Haiyang Li, Haitao Liu, Jiangchuan Xu, Ke
Abstract	Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents 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 online video services. To better understand this paradigm shift, we conduct a long-term and extensive measurement of video sharing in RenRen, the largest Facebook-like OSN in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that the video popularity distribution exhibits perfect power-law feature (while videos in YouTube exhibit a power-law waist with a long truncated tail). Moreover, we observe that the requests for the new published videos generally experience two or three days latency to reach the peak value, and then change dynamically with a series of unpredictable bursts (while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses generally decrease overtime, except possibly on some special days). These differences can raise new challenges to content providers. For example, the video popularity is now hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features.
Starting Page	83
Ending Page	88
Page Count	6
File Format	PDF
ISBN	9781450314305
DOI	10.1145/2229087.2229110
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Video sharing Popularity distribution Social network Power-law Popularity evolution
Content Type	Text
Resource Type	Article

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