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.
A number of studies have also looked at online gender swapping more generally @cite_24 . For example, @cite_30 @cite_18 found that gender swapping in online gaming is commonplace. The reasons for such activities have also been explored via surveys @cite_1 . Reasons given include: curiosity and the desire to experiment; the perception that the opposite gender is treated better; and the belief that playing the opposite gender will allow new forms of behaviour, or gain advantages. This complements our own work, although we do not focus on finding the reasons for users to catfish. Other general work on identifying deception in online communication include linking authors across OSNs @cite_25 , detecting online personas @cite_17 , and identifying divergent political inclinations @cite_0 .
@cite_30 @cite_18 @cite_1 @cite_24 @cite_0 @cite_25 @cite_17
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Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds. ABSTRACT Massively multiplayer online role-playing games (MMORPGs) are one of the most interesting innovations in the area of online computer gaming. Given the relative lack of research in the area, the main aims of the study were to examine (a) the impact of online gaming (e.g., typical playing behavior) in the lives of online gamers, (b) the effect of online socializing in the lives of gamers, and (c) why people engage in gender swapping. A self-selecting sample of 119 online gamers ranging from 18 to 69 years of age (M = 28.5 years) completed a questionnaire. The results showed that just over one in five gamers (21 ) said they preferred socializing online to offline. Significantly more male gamers than female gamers said that they found it easier to converse online than offline. It was also found that 57 of gamers had engaged in gender swapping, and it is suggested that the online female persona has a number of positive social attributes in a male-oriented environment. As players craft and enact identities in digital games, the relationship between player and avatar gender remains unclear. This study examines how 11 in-game chat, movement, and appearance behaviors differed by gender and by men who did and did not use a female avatar – or ‘gender-switchers’. Drawing on social role and feminist theories of gender, we argue that gender differences in behavior align with the social roles and norms that establish appropriate and inappropriate behavior for men and women. Thus we complicate questions of ‘gender-switching’ by examining not only player gender, but also player psychological Gender Role as measured by the Bem Sex Role Inventory to examine how gender does – and does not – manifest in digital worlds. Analysis revealed that men may not necessarily seek to mask their offline gender when they use a female avatar, but there is evidence they do reinforce idealized notions of feminine appearance and communication. Movement behaviors, however, show no differences across me... The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people’s word choices when fabricating lies.