short-paper

Measurement and analysis of an adult video streaming service

Authors:

Aniket MahantiAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 489 - 492

https://doi.org/10.1145/3341161.3342940

Published: 15 January 2020 Publication History

Abstract

Pornography can be distributed in multiple forms on the Internet. Online pornography forms a non-negligible fraction of the total Internet traffic, with adult video streaming gaining significant traction among the most visited global websites. Similar to the rise of User Generated Content (UGC) on general Web 2.0 services, adult video service providers have also promoted social interaction and UGC in what is called 'Porn 2.0'. Discovering the characteristics of Porn 2.0 allows for better understanding of both Internet traffic in general and specifically UGC services. In this paper, using trace-driven analysis, we examined the characteristics of one of the most well-known Porn 2.0 service providers, XHamster. We found that a large proportion of the currently available videos were uploaded in recent years and this has coincided with a rapid growth in the use of video categories. Compared to non-adult UGC services, we found user interaction on XHamster to revolve more strongly around ratings than comments and the average duration and views per video were higher.

References

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PornHub Insights, "2017 Year in Review" [Accessed 18-July-2019] www.pornhub.com/insights/2017-year-in-review

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A. Hawkins "XHamster Trend Report 2018" [Accessed 18-July-2019] xhamster.com/blog/posts/745297

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B. Farrelly, Y. Sun, A. Mahanti and M. Gong, "Video Workload Characteristics of Online Porn: Perspectives from a Major Video Streaming Service," in Proceedings of the 2017 IEEE 42nd Conference on Local Computer Networks (LCN), pp. 518--519, 2017.

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Digital Library

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Digital Library

Cited By

Rautela KSharma DKumar VKumar D(2024)Obscenity detection transformer for detecting inappropriate contents from videosMultimedia Tools and Applications10.1007/s11042-023-16078-283:4(10799-10814)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-16078-2
Ansari MPal KGovil PGovil M(2023)A fuzzy based hierarchical flash crowd controller for live video streaming in P2P networkPeer-to-Peer Networking and Applications10.1007/s12083-023-01463-316:2(1027-1048)Online publication date: 1-Mar-2023
https://doi.org/10.1007/s12083-023-01463-3
Jain GMahara TSharma SSangaiah A(2022)A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.31874309:6(1785-1793)Online publication date: Dec-2022
https://doi.org/10.1109/TCSS.2022.3187430
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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 January 2020

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Rautela KSharma DKumar VKumar D(2024)Obscenity detection transformer for detecting inappropriate contents from videosMultimedia Tools and Applications10.1007/s11042-023-16078-283:4(10799-10814)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-16078-2
Ansari MPal KGovil PGovil M(2023)A fuzzy based hierarchical flash crowd controller for live video streaming in P2P networkPeer-to-Peer Networking and Applications10.1007/s12083-023-01463-316:2(1027-1048)Online publication date: 1-Mar-2023
https://doi.org/10.1007/s12083-023-01463-3
Jain GMahara TSharma SSangaiah A(2022)A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.31874309:6(1785-1793)Online publication date: Dec-2022
https://doi.org/10.1109/TCSS.2022.3187430
Wong CSong YMahanti A(2020)YouTube of porn: longitudinal measurement, analysis, and characterization of a large porn streaming serviceSocial Network Analysis and Mining10.1007/s13278-020-00661-810:1Online publication date: 29-Jul-2020
https://doi.org/10.1007/s13278-020-00661-8

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