Raymond Yu, Callan Christophersen, Yo-Der Song, Aniket Mahanti
{"title":"成人视频流媒体服务的比较分析:特点和工作量","authors":"Raymond Yu, Callan Christophersen, Yo-Der Song, Aniket Mahanti","doi":"10.23919/TMA.2019.8784688","DOIUrl":null,"url":null,"abstract":"With the Internet continuing to produce more Web 2.0 services and online adult content seemingly taking up a large proportion of the Internet traffic, pornography services have embraced the shift to user generated content (UGC). This has allowed for more user engagement, forming the so called Porn 2.0. By investigating the characteristics of UGC porn, we can better understand how users are interacting with these streaming services. However, due to the taboo nature of pornography, there has been little work done in this area. In this paper, we examine three of the most consistently popular Porn 2.0 services: PornHub, xHamster, and YouPorn. Using a video corpus of nearly 3 million videos spanning over 10 years, we find that adult videos are commented and rated much less frequently than they are viewed, videos are significantly shorter than the typical TV show, and closer in duration to that of the typical YouTube video. The views for the videos are skewed towards a few popular videos. Video injection rate was variable across the sites. All three sites have on average far more ratings than comments. Video comments are distributed as per the Pareto principle, where a small number of videos receive vastly more comments than the other videos. Most videos receive a moderate number of tags with some more popular videos receiving higher numbers of tags.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Comparative Analysis of Adult Video Streaming Services: Characteristics and Workload\",\"authors\":\"Raymond Yu, Callan Christophersen, Yo-Der Song, Aniket Mahanti\",\"doi\":\"10.23919/TMA.2019.8784688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the Internet continuing to produce more Web 2.0 services and online adult content seemingly taking up a large proportion of the Internet traffic, pornography services have embraced the shift to user generated content (UGC). This has allowed for more user engagement, forming the so called Porn 2.0. By investigating the characteristics of UGC porn, we can better understand how users are interacting with these streaming services. However, due to the taboo nature of pornography, there has been little work done in this area. In this paper, we examine three of the most consistently popular Porn 2.0 services: PornHub, xHamster, and YouPorn. Using a video corpus of nearly 3 million videos spanning over 10 years, we find that adult videos are commented and rated much less frequently than they are viewed, videos are significantly shorter than the typical TV show, and closer in duration to that of the typical YouTube video. The views for the videos are skewed towards a few popular videos. Video injection rate was variable across the sites. All three sites have on average far more ratings than comments. Video comments are distributed as per the Pareto principle, where a small number of videos receive vastly more comments than the other videos. Most videos receive a moderate number of tags with some more popular videos receiving higher numbers of tags.\",\"PeriodicalId\":241672,\"journal\":{\"name\":\"2019 Network Traffic Measurement and Analysis Conference (TMA)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Network Traffic Measurement and Analysis Conference (TMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/TMA.2019.8784688\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Network Traffic Measurement and Analysis Conference (TMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/TMA.2019.8784688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative Analysis of Adult Video Streaming Services: Characteristics and Workload
With the Internet continuing to produce more Web 2.0 services and online adult content seemingly taking up a large proportion of the Internet traffic, pornography services have embraced the shift to user generated content (UGC). This has allowed for more user engagement, forming the so called Porn 2.0. By investigating the characteristics of UGC porn, we can better understand how users are interacting with these streaming services. However, due to the taboo nature of pornography, there has been little work done in this area. In this paper, we examine three of the most consistently popular Porn 2.0 services: PornHub, xHamster, and YouPorn. Using a video corpus of nearly 3 million videos spanning over 10 years, we find that adult videos are commented and rated much less frequently than they are viewed, videos are significantly shorter than the typical TV show, and closer in duration to that of the typical YouTube video. The views for the videos are skewed towards a few popular videos. Video injection rate was variable across the sites. All three sites have on average far more ratings than comments. Video comments are distributed as per the Pareto principle, where a small number of videos receive vastly more comments than the other videos. Most videos receive a moderate number of tags with some more popular videos receiving higher numbers of tags.