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Cited by 17Spam and Phishing DetectionCybercrime and Law Enforcement StudiesMisinformation and Its Impacts

Automatically Dismantling Online Dating Fraud

Open access
Suarez-Tangil, Guillermo, Edwards, Matthew, Peersman, Claudia, Stringhini, Gianluca, Rashid, Awais, Whitty, Monica·2020·IEEE Transactions on Information Forensics and Security
DOI 10.1109/tifs.2019.2930479
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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 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. Furthermore, 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. This paper 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.

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  • Works by Suarez-Tangil et al. and He et al. independently confirmed the benefit of using an ensemble classifier that combines predictions of the single classifiers that comprise the fraud detection system.

    “Works by Suarez-Tangil et al. (2019) and He et al. (2021) independently confirmed the benefit of using an ensemble classifier that combines predictions of the single classifiers that comprise the fraud detection system.”

    From Tainted Love: a Systematic Literature Review of Online Romance Scam Research (2023) · cites this paper as Suarez-Tangil et al. (2019)

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Research works printed but not linked: 20

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Cited by

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Authors
Suarez-Tangil, G., Edwards, M., Peersman, C., Stringhini, G., Rashid, A., & Whitty, M.
Affiliations
  • University of Bristol (GB)
  • Boston University (US)
  • The University of Melbourne (AU)
ORCID
Venue
IEEE Transactions on Information Forensics and Security
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
15
Pages
1128-1137
ISSN
  • 1556-6013 (ISSN-L)
  • 1556-6021
DOI
10.1109/tifs.2019.2930479
OpenAlex
W2947544539
Cited by
17
References
6
Citations by year
2026: 4 · 2025: 1 · 2024: 3 · 2023: 4 · 2020: 2 · 2019: 3
FWCI
1.56
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Information Forensics and Security (publishedVersion)
  • Explore Bristol Research (publishedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion, other-oa)
  • Minerva Access (University of Melbourne) (submittedVersion)
  • OpenBU (Boston University) (submittedVersion)
  • Bristol Research (University of Bristol) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
Engineering and Physical Sciences Research Council (EP/N028112/1)
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
online dating fraud, romance scams, fraud detection, victim characteristics, profile features, ensemble learning, deep features, dating websites

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