Fake it till you make it Fishing for Catfishes

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 catfis...

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Published in:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) pp. 497 - 504
Main Authors: Magdy, Walid, Elkhatib, Yehia, Tyson, Gareth, Joglekar, Sagar, Sastry, Nishanth
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 31.07.2017
Series:ACM Conferences
Subjects:
ISBN:1450349935, 9781450349932
ISSN:2473-991X
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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. When applying our models for age and gender estimation to unverified profiles, 38% of profiles are classified as lying about their age, and 25% are predicted to be lying about their gender. The results suggest that women have a greater propensity to catfish than men. Our preliminary work has notable implications on operators of such online social networks, as well as users who may worry about interacting with catfishes.
AbstractList 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. When applying our models for age and gender estimation to unverified profiles, 38% of profiles are classified as lying about their age, and 25% are predicted to be lying about their gender. The results suggest that women have a greater propensity to catfish than men. Our preliminary work has notable implications on operators of such online social networks, as well as users who may worry about interacting with catfishes.
Author Magdy, Walid
Joglekar, Sagar
Tyson, Gareth
Elkhatib, Yehia
Sastry, Nishanth
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  givenname: Yehia
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  fullname: Sastry, Nishanth
  email: nishanth.sastry@kcl.ac.uk
  organization: Department of Informatics, King's College London, UK
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Snippet Many adult content websites incorporate social networking features. Although these are popular, they raise significant challenges, including the potential for...
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StartPage 497
SubjectTerms Computing methodologies
Computing methodologies -- Machine learning
Computing methodologies -- Machine learning -- Learning paradigms
Computing methodologies -- Machine learning -- Learning paradigms -- Multi-task learning
Human-centered computing
Human-centered computing -- Collaborative and social computing
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods -- Social network analysis
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social media
Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social networks
Information systems
Information systems -- World Wide Web
Information systems -- World Wide Web -- Web searching and information discovery
Networks
Networks -- Network types
Networks -- Network types -- Overlay and other logical network structures
Networks -- Network types -- Overlay and other logical network structures -- Online social networks
Social and professional topics
Social and professional topics -- User characteristics
Social and professional topics -- User characteristics -- Gender
Subtitle Fishing for Catfishes
Title Fake it till you make it
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