“Fake it till you make it: Fishing for Catfishes” by Walid Magdy et al.

Using language algorithms  to detect fake online profiles that deceive other users

Abstract

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.

Paper to appear in IEEE/ACM ASONAM 2017 https://arxiv.org/abs/1705.06530

Dr Walid Magdy, University of Edinburgh, School of Informatics.

Dr Lubie Alatriste

Lubie Alatriste

Affiliation : NYC College of Technology, City University of New York (CUNY)

Lubie G. Alatriste is associate professor in the Department of English, City University of New York. She currently teaches second language writing, composition, and courses in literacy and linguistics. Her research focuses on genre teaching and transfer as well as critical discourse. Most recently she has developed a framework for application of discourse research results in professional practice. Her most recent publications appeared in Journal of Second Language Writing, Idiom, and NYSTESOL Journal. Her most recent book is an edited collection by Multilingual Matters, UK.

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