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Publication - Professor Awais Rashid

    Automatically dismantling online dating fraud


    Suarez-Tangil, G, Edwards, M, Peersman, C, Stringhini, G, Rashid, A & Whitty, M, 2019, ‘Automatically dismantling online dating fraud’. IEEE Transactions on Information Forensics and Security.


    Online romance scams are a prevalent form of mass-marketing fraud in the West,
    and yet few studies have presented data-driven responses to this problem. In
    this type of scam, fraudsters craft fake profiles and manually interact with
    their victims. Because of the characteristics of this type of fraud and of how
    dating sites operate, traditional detection methods (e.g., those used in spam
    filtering) are ineffective. In this paper, we investigate the archetype of
    online dating profiles used in this form of fraud, including their use of
    demographics, profile descriptions, and images, shedding light on both the
    strategies deployed by scammers to appeal to victims and the traits of victims
    themselves. Further, in response to the severe financial and psychological harm
    caused by dating fraud, we develop a system to detect romance scammers on online
    dating platforms.

    Our work presents the first fully described system for automatically detecting
    this fraud. Our aim is to provide an early detection system to stop romance
    scammers as they create fraudulent profiles or before they engage with potential
    victims. Previous research has indicated that the victims of romance scams score
    highly on scales for idealized romantic beliefs. We combine a range of
    structured, unstructured, and deep-learned features that capture these beliefs
    in order to build a detection system. Our ensemble machine-learning approach is
    robust to the omission of profile details and performs at high accuracy (97\%)
    in a hold-out validation set. The system enables development of automated tools
    for dating site providers and individual users.

    Full details in the University publications repository