Many adult content websites incorporate social networking features. Although these are popular, they raise significant challenges, including the potential for users to "catfish", i.e., to create fake profiles to deceive other users. This paper takes an initial step towards automated catfish detection. We explore the characteristics of the different age and gender groups, identifying a number of distinctions. Through this, we train models based on user profiles and comments, via the ground truth of specially verified profiles. Applying our models for age and gender estimation of unverified profiles, we identify 38 of profiles who are likely lying about their age, and 25 who are likely lying about their gender. We find that women have a greater propensity to catfish than men. Further, whereas women catfish select from a wide age range, men consistently lie about being younger. Our work has notable implications on operators of such online social networks, as well as users who may worry about interacting with catfishes.
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@cite_21: This paper investigates web users and their sexual behaviors and pornographic self-representations as observed on the sex and dating site http: www.adultfrienfinder.com. The website is a social network and encourages members to find real-life partners for sex whether it be casual sex affairs between singles, swinging couples, or polyamorous extra-marital affairs between “aba” (“attached but available”) individuals and their lovers. The analysis is based on theories of ethnography and social networking and analyzes the effects of corporate networks and homogenizing “sex scripts” on sex lives and Internet culture in Hong Kong.
@cite_2: Although according to surveys related to internet user activity it is considered one of the most popular aspects, few studies are actually concerned with internet pornography. This paper is aimed at rectifying that overlook. In particular, we study user activity related to internet pornography by looking at two main behaviors: (i) watching pornography, and (ii) providing feedback on pornography items in the form of ratings and comments. By using appropriate datasets that we collect, we make contributions related to the study of both behaviors pointed out above. With regards to viewing, we observe that views are highly dependent on pornography category and video size. By studying the feedback system of pornography video websites, we observe differences in the way users rate items across websites popular in different parts of the world. Finally, we employ sentiment analysis to study the comments that users leave on pornography websites and we find surprising similarities across the analyzed websites. Our results pave the way to understanding more about human behavior related to internet pornography and can impact, among others, fields such as content personalization, video content delivery, recommender systems
@cite_5: Online obscenity and myths of freedom : dangerous images, child porn, and neoliberalism Stephen Maddison -- Child pornography : classifications and conceptualizations Adam Stapleton -- Debbie does Dallas again and again : pornography, technology, and market innovation David Slayden -- Porn 2.0 : technology, social practice, and the new online porn industry Sharif Mowlabocus -- Younger, paler, decidedly less straight : the new porn professionals Feona Attwood -- Behind the scenes of straight pleasure Sanna Harma and Joakim Stolpe -- Horrorporn pornhorror : the problematic communities and contexts of extreme online shock imagery Steven Jones -- Good amateurs : erotica writing and notions of quality Susanna Paasonen -- Gay for pay, gay for(e)play : the politics of taxonomy and authenticity in LGBTQ online porn Jennifer Moorman -- Widening the glory hole : the discourse of online porn fandom Simon Lindgren -- The new world dream and the female itch : sex blogging and Lolita costume play in Hong Kong, Taiwan, and China Katrien Jacobs -- How do I rate? : web sites and gendered erotic looking glasses Dennis D. Waskul and Cheryl L. Radeloff -- Beyond "key parties" and "wife swapping" : the visual culture of online swinging Alison Rooke and Monica Moreno Figueroa -- Conclusion: Toward the study of online porn cultures and practices Feona Attwood.
@cite_31: 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.
@cite_13: YouPorn is one of the largest providers of adult content on the web. Being free of charge, the video portal allows users - besides watching - to upload, categorize, and comment on pornographic videos. With this position paper, we point out the challenges of analyzing the textual data offered with the videos. We report on first experiments and problems with our YouPorn dataset , which we extracted from the non-graphical content of the YP website. To gain some insights, we performed association rule mining on the video categories and tags, and investigated preferences of users based on their nickname. Hoping that future research will be able to build upon our initial experiences, we make the ready-to-use YP dataset publicly available.
To the best of our knowledge, there has been no prior work focusing on demographic verification within the adult domain, although various works have focused on related components of Porn 2.0, such as pornographic practices, communities and subcultures @cite_5 ; interest recommendations @cite_13 ; dating services @cite_21 @cite_8 ; user commenting @cite_2 ; content popularity @cite_31 and illegal content dissemination @cite_14 . The closest to our work is a recent study of the PornHub social network @cite_22 , although it did not touch upon age verification.