I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system

By Meeyoung Cha, Haewoon Kwak, Pablo Rodríguez Rodríguez, Yong-Yeol Ahn, Sue B. Moon. ACM/SIGCOMM Internet Measurement Conference, 2007

1,753 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7b12113494cb5c6d5fa092c5e614fd4e5f5be606

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Citations in the corpus

Data Offloading Techniques in Cellular Networks: A Survey (2015, positive): “However, this proves to be relevant for content access, as popularity follows Zipf-like distributions [113]—a small subset of content results extremely popular and is requested by a large number of co-located users, causing severe congestion and bandwidth”
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge (2018, background): “For instance, many real-world phenomena– such as video popularity, webpage click frequency, word frequency in documents, node degrees in social networks– follow power-law distributions [9], [10]; height of human follows a Gaussian distribution [2].”
An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets (2013, positive): “The usefulness of popularity or preference indicators in predicting subsequent product popularity has been demonstrated previously within certain online contexts, such as YouTube (Cha et al. 2007; Duan et al. 2009; Tucker and Zhang 2011).”
Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube (2012, background): “Given the limited attention span of viewers and fluctuations in daily viewership of videos (Szabo and Huberman 2009), popularity of usergenerated content is relatively ephemeral (e.g., Cha et al. 2007).”
Coded caching with nonuniform demands (2013, methodology): “Following the a pproach in [17], we estimatepn from the dataset made available by Netflix for the Netflix Priz e [18].”
Why do people watch others play video games? An empirical study on the motivations of Twitch users (2017, result): “With the rise of user-generated content as one of themain forms of contemporary media, as discussed earlier (Cha et al., 2007), we find the results of this study to offer deeper insight not only into broadcast media consumption, but also into how consumers approach games as a medium.”
YouTube everywhere: impact of device and infrastructure synergies on user experience (2011, background): “On the other hand, [3] and [4] crawled the YouTube site for an extended period of time and performed video popularity and user behavior analysis.”
Caching performance of content centric networks under multi-path routing (and more) (2011, background): “Concerning caching policies, as pointed out in [14], much attention has been devoted to cache replacement issues (over 40 policies are overviewed in [7]), while cache decision policies have being studied sporadically (to the best of our knowledge, only by [14]–[17]).”
Chic or Social: Visual Popularity Analysis in Online Fashion Networks (2014, background): “Work in this direction has mainly looked into early social reaction to content and the prediction of popularity growth in videos [5, 16, 14, 3, 4], news [10, 18], and discussion forums [9].”
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems (2009, background): “In this paper and an early version of this work (which appeared in the ACM Internet Measurement Conference in 2007 [19]), we have presented a comprehensive analysis of the popularity distribution and the time evolution of UGC video requests and their implications.”
Key differences between Web 1.0 and Web 2.0 (2008, background): “These studies have tried to analyze several aspects, such as the number of views and rankings, and local (geographic) popularity of videos over time [6, 31]; object sizes and access patterns [11]; and properties of the embedded social network such as degree and cluster coefficient [21].”
Inside dropbox: understanding personal cloud storage services (2012, background): “Inoneofourdatasets, for instance, Dropbox isalready equivalent to one third of the YouTube tra.c.”