Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tifs.2019.2930479 | IEEE Transactions on Information Forensics and Security |
DocType | Volume | Citations |
Journal | abs/1905.12593 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillermo Suarez-Tangil | 1 | 180 | 12.44 |
| Matthew John Edwards | 2 | 26 | 5.95 |
| Claudia Peersman | 3 | 109 | 8.21 |
| Gianluca Stringhini | 4 | 701 | 61.87 |
| Awais Rashid | 5 | 2041 | 149.78 |
| Monica T. Whitty | 6 | 170 | 15.67 |