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Automatically Dismantling Online Dating Fraud

Research output: Contribution to journalArticle

Guillermo Suarez-Tangil, Matthew Edwards, Claudia Peersman, Gianluca Stringhini, Awais Rashid, Monica Whitty

Original languageEnglish
Article number8768406
Pages (from-to)1128-1137
Number of pages10
JournalIEEE Transactions on Information Forensics and Security
Volume15
DOIs
Publication statusPublished - 22 Jul 2019

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Abstract

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 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. Furthermore, 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. This paper 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.

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