Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems

By Meeyoung Cha, Haewoon Kwak, Pablo Rodríguez Rodríguez, Yong-Yeol Ahn, Sue B. Moon. IEEE/ACM Transactions on Networking, 2009

509 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/515d1534fcc4115cf9e2b55335d49826fa961915

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Citations in the corpus

Using early view patterns to predict the popularity of youtube videos (2013, methodology): “…Subject Descriptors C.4 [Computer Systems Organization]: Performance of Systems - Measurement techniques; H.3.5 [Information Storage and Retrieval]: Online Information Services - Web-based services General Terms Experimentation, Measurement Keywords popularity prediction; YouTube; regression models”
Social Clicks: What and Who Gets Read on Twitter? (2016, unknown): “However, this study considered clicks originating from any sources, including YouTube own recommendation systems, search engine and other mentions online.”
From Scaling Disparities to Integrated Parallelism: A Decathlon for a Decade (2017, background): “less popular content still being a substantial fraction of the total traffic; for example [26], [46], up to ∼10% of the content may account for∼80% of the requests, while the remaining∼90% of the content is still being requested ∼20% of the time.”
Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning (2018, background): “Furthermore, recent studies reveal that different contents require different levels of priority [4], [5].”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “For instance, in Reference [28], the statistical properties of popularity distributions of requesting contents are analyzed and the opportunities utilizing the latent request for “the Long Tail” potential are discussed.”