Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Recognition of Pornographic Web Pages by Classifying Texts and Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The stretched exponential distribution of internet media access patterns
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Watching television over an IP network
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analyzing the video popularity characteristics of large-scale user generated content systems
IEEE/ACM Transactions on Networking (TON)
Predicting the popularity of online content
Communications of the ACM
Proceedings of the ACM SIGCOMM 2010 conference
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
The untold story of the clones: content-agnostic factors that impact YouTube video popularity
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Program popularity and viewer behaviour in a large TV-on-demand system
Proceedings of the 2012 ACM conference on Internet measurement conference
Using early view patterns to predict the popularity of youtube videos
Proceedings of the sixth ACM international conference on Web search and data mining
Measurement and analysis of child pornography trafficking on P2P networks
Proceedings of the 22nd international conference on World Wide Web
Understanding and decreasing the network footprint of catch-up tv
Proceedings of the 22nd international conference on World Wide Web
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The Internet has evolved into a huge video delivery infrastructure, with websites such as YouTube and Netflix appearing at the top of most traffic measurement studies. However, most traffic studies have largely kept silent about an area of the Internet that (even today) is poorly understood: adult media distribution. Whereas ten years ago, such services were provided primarily via peer-to-peer file sharing and bespoke websites, recently these have converged towards what is known as ``Porn 2.0''. These popular web portals allow users to upload, view, rate and comment videos for free. Despite this, we still lack even a basic understanding of how users interact with these services. This paper seeks to address this gap by performing the first large-scale measurement study of one of the most popular Porn 2.0 websites: YouPorn. We have repeatedly crawled the website to collect statistics about 183k videos, witnessing over 60 billion views. Through this, we offer the first characterisation of this type of corpus, highlighting the nature of YouPorn's repository. We also inspect the popularity of objects and how they relate to other features such as the categories to which they belong. We find evidence for a high level of flexibility in the interests of its user base, manifested in the extremely rapid decay of content popularity over time, as well as high susceptibility to browsing order. Using a small-scale user study, we validate some of our findings and explore the infrastructure design and management implications of our observations.