Automatically Dismantling Online Dating Fraud

Guillermo Suarez-Tangil

Abstract:

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.