skip to main content
research-article

Measurements and Analysis of a Major Adult Video Portal

Published:28 January 2016Publication History
Skip Abstract Section

Abstract

Today, the Internet is a large multimedia delivery infrastructure, with websites such as YouTube appearing at the top of most measurement studies. However, most traffic studies have ignored an important domain: adult multimedia distribution. Whereas, traditionally, such services were provided primarily via bespoke websites, recently these have converged towards what is known as “Porn 2.0”. These services allow users to upload, view, rate, and comment on videos for free (much like YouTube). Despite their scale, we still lack even a basic understanding of their operation. This article addresses this gap by performing a large-scale study of one of the most popular Porn 2.0 websites: YouPorn. Our measurements reveal a global delivery infrastructure that we have repeatedly crawled to collect statistics (on 183k videos). We use this data to characterise the corpus, as well as to inspect popularity trends and how they relate to other features, for example, categories and ratings. To explore our discoveries further, we use a small-scale user study, highlighting key system implications.

References

  1. Henrik Abrahamsson and Mattias Nordmark. 2012. Program popularity and viewer behaviour in a large TV-on-demand system. In Proceedings of IMC. ACM, New York, NY, 199--210. DOI:http://dx.doi.org/10.1145/2398776.2398798 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alexa. 2014. Alexa Website Rankings. http://www.alexa.com/. (2014).Google ScholarGoogle Scholar
  3. Sebastian Anthony. 2012. Just How Big are Porn Sites? Retrieved from http://www.extremetech.com/computing/123929-just-how-big-are-porn-sites.Google ScholarGoogle Scholar
  4. John G. Apostolopoulos, Wai-tian Tan, and Susie J. Wee. 2002. Video Streaming: Concepts, Algorithms, and Systems. Report HPL-2002-260. HP Laboratories.Google ScholarGoogle Scholar
  5. Feona Attwood. 2010. Porn.com: Making Sense of Online Pornography. Vol. 48. Peter Lang.Google ScholarGoogle Scholar
  6. Youmna Borghol, Sebastien Ardon, Niklas Carlsson, Derek Eager, and Anirban Mahanti. 2012. The untold story of the clones: Content-agnostic factors that impact YouTube video popularity. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1186--1194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Andrew Brampton, Andrew MacQuire, Michael Fry, Idris A. Rai, Nicholas J. P. Race, and Laurent Mathy. 2009. Characterising and exploiting workloads of highly interactive video-on-demand. Multimedia Systems 15, 1 (2009), 3--17.Google ScholarGoogle ScholarCross RefCross Ref
  8. Matt Calder, Xun Fan, Zi Hu, Ethan Katz-Bassett, John Heidemann, and Ramesh Govindan. 2013. Mapping the expansion of Google’s serving infrastructure. In Proceedings of the 2013 Conference on Internet Measurement Conference. ACM, 313--326. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Jason S. Carroll, Laura M. Padilla-Walker, Larry J. Nelson, Chad D. Olson, Carolyn McNamara Barry, and Stephanie D. Madsen. 2008. Generation XXX pornography acceptance and use among emerging adults. Journal of Adolescent Research 23, 1 (2008), 6--30.Google ScholarGoogle ScholarCross RefCross Ref
  10. Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon. 2009. Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Transactions on Networking 17, 5 (2009), 1357--1370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Meeyoung Cha, Pablo Rodriguez, Jon Crowcroft, Sue Moon, and Xavier Amatriain. 2008. Watching television over an IP network. In Proceedings of IMC. ACM, 71--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Gloria Chatzopoulou, Cheng Sheng, and Michalis Faloutsos. 2010. A first step towards understanding popularity in YouTube. In INFOCOM Workshops. IEEE, 1--6. DOI:http://dx.doi.org/10.1109/INFCOMW.2010.5466701Google ScholarGoogle ScholarCross RefCross Ref
  13. Xu Cheng, Cameron Dale, and Jiangchuan Liu. 2008. Statistics and social network of YouTube videos. In Proceedings of the16th International Workshop on Quality of Service (IWQoS’08). IEEE, 229--238.Google ScholarGoogle ScholarCross RefCross Ref
  14. Cisco. 2015. Cisco Visual Networking Index: Forecast and Methodology, 2014--2019. (May 2015).Google ScholarGoogle Scholar
  15. comSCORE. 2014. Online Video Rankings. Retrieved from http://www.comscore.com/Insights/Press-Releases/2014/2/comScore-Releases-January-2014-US-Online-Video-Rankings.Google ScholarGoogle Scholar
  16. Al Cooper. 1998. Sexuality and the Internet: Surfing into the new millennium. CyberPsychology & Behavior 1, 2 (January 1998), 187--193.Google ScholarGoogle ScholarCross RefCross Ref
  17. Jonathan Coopersmith. 2006. Does your mother know what you really do? The changing nature and image of computer-based pornography. History and Technology 22, 1 (2006), 1--25.Google ScholarGoogle ScholarCross RefCross Ref
  18. Riley Crane and Didier Sornette. 2008. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences 105, 41 (2008), 15649--15653.Google ScholarGoogle ScholarCross RefCross Ref
  19. Sally Jo Cunningham and David M. Nichols. 2008. How people find videos. In Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM, 201--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kristian Daneback, Anna Sevcikova, Sven-Axel Månsson, and Michael W. Ross. 2012. Outcomes of using the Internet for sexual purposes: Fulfilment of sexual desires. Sexual Health 10, 1 (Dec. 2012), 26--31. DOI:http://dx.doi.org/10.1071/SH11023Google ScholarGoogle Scholar
  21. Gail Dines. 2010. Pornland: How Porn Has Hijacked Our Sexuality. Beacon Press.Google ScholarGoogle Scholar
  22. Yehia Elkhatib, Rebecca Killick, Mu Mu, and Nicholas Race. 2014. Just browsing? Understanding user journeys in online TV. In Proceedings of the ACM Conference on Multimedia. 965--968. DOI:http://dx.doi.org/10.1145/2647868.2654980 Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Lei Guo, Enhua Tan, Songqing Chen, Zhen Xiao, and Xiaodong Zhang. 2008. The stretched exponential distribution of internet media access patterns. In Proceedings of PODC. ACM, 283--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Xiaojun Hei, Chao Liang, Jian Liang, Yong Liu, and Keith W. Ross. 2007. A measurement study of a large-scale P2P IPTV system. IEEE Transactions on Multimedia 9, 8 (2007), 1672--1687. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Weiming Hu, Ou Wu, Zhouyao Chen, Zhouyu Fu, and Steve Maybank. 2007. Recognition of pornographic web pages by classifying texts and images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 6 (June 2007), 1019--1034. DOI:http://dx.doi.org/10.1109/TPAMI.2007.1133 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ryan Hurley, Swagatika Prusty, Hamed Soroush, Robert J. Walls, Jeannie Albrecht, Emmanuel Cecchet, Brian Neil Levine, Marc Liberatore, Brian Lynn, and Janis Wolak. 2013. Measurement and analysis of child pornography trafficking on P2P networks. In Proceedings of the World Wide Web Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Anna Kupka. 2010. YouTube Reaches 4 Billion Video Views Per Day. Retrieved from http://www.forbes.com/sites/annakupka/2012/01/24/youtube-reaches-4-billion-video-views-per-day/.Google ScholarGoogle Scholar
  28. Patricia G. Lange. 2007. Commenting on comments: Investigating responses to antagonism on YouTube. In Annual Conference of the Society for Applied Anthropology (2007).Google ScholarGoogle Scholar
  29. Lassi A. Liikkanen and Antti Salovaara. 2015. Music on YouTube: User engagement with traditional, user-appropriated and derivative videos. Computers in Human Behavior 50 (2015), 108--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Michael D. Mehta and Dwaine Plaza. 1997. Content analysis of pornographic images available on the Internet. The Information Society 13, 2 (1997), 153--161. DOI:http://dx.doi.org/10.1080/019722497129179Google ScholarGoogle ScholarCross RefCross Ref
  31. Gianfranco Nencioni, Nishanth Sastry, Gareth Tyson, Vijay Badrinarayanan, Dmytro Karamshuk, Jigna Chandaria, and Jon Crowcroft. 2015. SCORE: Exploiting global broadcasts to create offline personal channels for on-demand access. IEEE/ACM Transactions on Networking (2015). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7210228.Google ScholarGoogle Scholar
  32. Ogi Ogas and Sai Gaddam. 2011. A Billion Wicked Thoughts: What the World’s Largest Experiment Reveals About Human Desire. Dutton.Google ScholarGoogle Scholar
  33. Henrique Pinto, Jussara M. Almeida, and Marcos A. Gonçalves. 2013. Using early view patterns to predict the popularity of YouTube videos. In Proceedings of the International Conference on Web Search and Data Mining. ACM, 365--374. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Nishanth Sastry. 2012. How to tell head from tail in user-generated content corpora. In Proceedings of the Conference on Weblogs and Social Media.Google ScholarGoogle Scholar
  35. Michael Schuhmacher, Cäcilia Zirn, and Johanna Völker. 2013. Exploring YouPorn categories, tags, and nicknames for pleasant recommendations. In Proceedings of the Workshop on Search and Exploration of X-Rated Information (SEXI 2013). 27--28.Google ScholarGoogle Scholar
  36. Hendrik Schulze and Klaus Mochalski. 2009. Internet Study 2007--2009. Retrieved from http://www.ipoque.com/resources/internet-studies/.Google ScholarGoogle Scholar
  37. Stefan Siersdorfer, Sergiu Chelaru, Wolfgang Nejdl, and Jose San Pedro. 2010. How useful are your comments?: Analyzing and predicting YouTube comments and comment ratings. In Proceedings of the 19th International Conference on the World Wide Web. ACM, 891--900. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. John Suler. 2004. The online disinhibition effect. Cyberpsychology & Behavior 7, 3 (2004), 321--326.Google ScholarGoogle ScholarCross RefCross Ref
  39. Gabor Szabo and Bernardo A. Huberman. 2010. Predicting the popularity of online content. Communications of the ACM 53, 8 (Aug. 2010), 80--88. DOI:http://dx.doi.org/10.1145/1787234.1787254 Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Mike Thelwall, Pardeep Sud, and Farida Vis. 2012. Commenting on YouTube videos: From Guatemalan rock to el big bang. Journal of the American Society for Information Science and Technology 63, 3 (2012), 616--629. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Gareth Tyson, Yehia Elkhatib, Nishanth Sastry, and Steve Uhlig. 2013. Demystifying porn 2.0: A look into a major adult video streaming website. In Proceedings of the 2013 Conference on Internet Measurement Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Gareth Tyson, Yehia Elkhatib, Nishanth Sastry, and Steve Uhlig. 2015. Are people really social on porn 2.0? In Proceedings of the 9th International AAAI Conference on Web and Social Media.Google ScholarGoogle Scholar
  43. Mirjam Wattenhofer, Yannet Interian, Jon Vaver, and Tom Broxton. 2010. Catching a viral video. In Proceedings of the Workshop on Social Interaction Analysis and Service Providers (SIASP’10), colocated with ICDM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Gilbert Wondracek, Thorsten Holz, Christian Platzer, Engin Kirda, and Christopher Kruegel. 2010. Is the Internet for porn? An insight into the online adult industry. In Proceedings of the Workshop on Economics of Information Security.Google ScholarGoogle Scholar
  45. YouPorn. 2012a. YouPorn 2012: BIG Numbers, HARD Facts. Retrieved from http://blog.youporn.com/youporn-2012-big-numbers-hard-facts/.Google ScholarGoogle Scholar
  46. YouPorn. 2012b. Youporn.com is now a 100% Redis Site. Retrieved from https://groups.google.com/forum/?fromgroups=#!topic/redis-db/d4QcWV0p-YM.Google ScholarGoogle Scholar
  47. YouPorn. 2014. YouPorn. Retrieved from http://www.youporn.com.Google ScholarGoogle Scholar
  48. YouPorn. 2015. YouPorn Content Partnership. Retrieved from http://www.youporn.com/contentpartnerprogram/makemoney/.Google ScholarGoogle Scholar
  49. YouTube. 2015. YouTube Statistics. Retrieved from http://www.youtube.com/yt/press/en-GB/statistics.html.Google ScholarGoogle Scholar
  50. Hongliang Yu, Dongdong Zheng, Ben Y. Zhao, and Weimin Zheng. 2006. Understanding user behavior in large-scale video-on-demand systems. In ACM SIGOPS Operating Systems Review, Vol. 40. ACM, 333--344. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Michael Zink, Kyoungwon Suh, Yu Gu, and Jim Kurose. 2009. Characteristics of YouTube network traffic at a campus network—Measurements, models, and implications. Computer Networks 53, 4 (2009), 501--514. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Measurements and Analysis of a Major Adult Video Portal

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 2
      March 2016
      224 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/2837041
      Issue’s Table of Contents

      Copyright © 2016 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 January 2016
      • Revised: 1 October 2015
      • Accepted: 1 October 2015
      • Received: 1 April 2015
      Published in tomm Volume 12, Issue 2

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!