Communications of the ACM
Software Tool to Detect Fake Online Profiles
Researchers have trained computer models to spot social media users who make up information about themselves — known as catfishes.
The system is designed to identify users who are dishonest about their age or gender. Scientists believe it could have potential benefits for helping to ensure the safety of social networks.
Spotting Fakes
Computer scientists at the University of Edinburgh built computer models designed to detect fake profiles on an adult content website. Sites of this type are believed to be heavily targeted by catfishes to befriend other users and gain more profile views.
The researchers describe their work in "Fake It Till You Make It: Fishing for Catfishes," to be presented at ASONAM 2017, the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
Researchers built their models based on information gleaned from about 5,000 verified public profiles on the site. These profiles were used to train the model to estimate the gender and age of a user with high accuracy, using their style of writing in comments and network activity.
This enabled the models to accurately estimate the age and gender of users with unverified accounts, and spot misinformation. All details were anonymized to protect users' privacy.
Dishonest Users
The study found that almost 40 percent of the site's users lie about their age and one-quarter lie about their gender, with women more likely to deceive than men.
The outcome, which underscores the extent of catfishing in adult networks, demonstrates the effectiveness of the technology in weeding out dishonest users.
"Adult websites are populated by users who claim to be other than who they are, so these are a perfect testing ground for techniques that identify catfishes," says Walid Magdy, assistant professor in the School of Informatics at Edinburgh. "We hope that our development will lead to useful tools to flag dishonest users and keep social networks of all kinds safe."
The ASONAM 2017 study is authored by Walid Magdy at the University of Edinburgh, Yehia Elkhatib of Lancaster University, Gareth Tyson at Queen Mary University of London, Sagar Joglekar, a fellow at King's College London, and Nishanth Sastry at King's College London.
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