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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 of 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. 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 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.
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 of 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. 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 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1128-1137 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Information Forensics and Security |
| Volume | 15 |
| DOIs | |
| Publication status | Published - 22 Jul 2019 |
Research Groups and Themes
- Cyber Security
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Dive into the research topics of 'Automatically dismantling online dating fraud'. Together they form a unique fingerprint.Projects
- 1 Finished
-
DAPM: Detecting and Preventing Mass-Marketing Fraud (MMF)
Rashid, A. (Principal Investigator), Edwards, M. (Researcher) & Peersman, C. (Researcher)
1/12/16 → 30/11/18
Project: Research
