Automatically dismantling online dating fraud
Files
Accepted manuscript
Date
2020
Authors
Suarez-Tangil, Guillermo
Edwards, Matthew
Peersman, Claudia
Stringhini, Gianluca
Rashid, Awais
Whitty, Monica
Version
Accepted manuscript
OA Version
Citation
Guillermo Suarez-Tangil, Matthew Edwards, Claudia Peersman, Gianluca Stringhini, Awais Rashid, Monica Whitty. 2020. "Automatically Dismantling Online Dating Fraud." IEEE Transactions on Information Forensics and Security, Volume 15, pp. 1128 - 1137. https://doi.org/10.1109/tifs.2019.2930479
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