  Online romance scams are a prevalent form of mass-marketing fraud in the
West, and yet few studies have addressed the technical or 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 present
the results of a multi-pronged investigation into 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 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. No prior work has fully analyzed whether these notions of romance
introduce traits that could be leveraged to build a detection system. Our
ensemble machine-learning approach is robust to the omission of profile details
and performs at high accuracy (97\%). The system enables development of
automated tools for dating site providers and individual users.
