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.
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.
@cite_14 @cite_22 @cite_8 @cite_21 @cite_2 @cite_5 @cite_31 @cite_13
2092079672 1914856875 223503673 2099216531
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. While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpus-specific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years. 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. Demographic information plays an important role in personalized web applications. However, it is usually not easy to obtain this kind of personal data such as age and gender. In this paper, we made a first approach to predict users' gender and age from their Web browsing behaviors, in which the Webpage view information is treated as a hidden variable to propagate demographic information between different users. There are three main steps in our approach: First, learning from the Webpage click-though data, Webpages are associated with users' (known) age and gender tendency through a discriminative model; Second, users' (unknown) age and gender are predicted from the demographic information of the associated Webpages through a Bayesian framework; Third, based on the fact that Webpages visited by similar users may be associated with similar demographic tendency, and users with similar demographic information would visit similar Webpages, a smoothing component is employed to overcome the data sparseness of web click-though log. Experiments are conducted on a real web click-through log to demonstrate the effectiveness of the proposed approach. The experimental results show that the proposed algorithm can achieve up to 30.4 improvements on gender prediction and 50.3 on age prediction in terms of macro F1, compared to baseline algorithms.