Skip to main content

A Game-Theoretic Approach to Detecting Romance Scams

Ebelechukwu Nwafor (2023) — New Perspectives in Behavioral Cybersecurity

Citation tools


              
              

Transparency

Evidence and review status

This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

Source basis
Downloaded PDF
Source updates
No notice found at last check
AI-generated page content
Yes
Automated checks
Passed
Administrative approval
Yes
Human content review
Not recorded
Subject-matter-expert review
Not recorded
How this was prepared
Source basis

RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.

  • PDF added to RSRC:
Source updates

No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.

  • Last source-status attempt:
AI-generated page content

AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.

  • Document analysis recorded:
  • Page record updated:
Automated checks

The current, source-bound synopsis passed the recorded versioned publication checks.

  • Checks completed:
View passed checks (3)
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
  • Numerical claims checked against the available source text
  • English-source lexical grounding check
Administrative approval

The record is approved for public display, but a complete historical administrator action is not recorded.

Human content review

No human review is recorded for the AI-generated content displayed on this page.

Subject-matter-expert review

RSRC has not recorded review of this content by a subject-matter or methods expert.

Review-state definitions
Found a possible error? Request a correction.

Synopsis

This conceptual chapter explains how game theory could model strategic interaction between romance scammers, targets, and detection systems. It argues that an automated detector informed by game-theoretic incentives and likely responses could help identify deception, reduce financial and personal-information theft, and improve trust in online dating services. The accessible abstract presents a modeling approach and anticipated benefits, not a completed deployment. It does not describe a dataset, implementation, comparison baseline, or measured detection performance. The chapter should therefore be treated as a proposed analytical direction for romance-scam detection rather than evidence that a particular automated system is effective.

Identified Gaps

The paper identifies the difficulty of scaling human moderation as online dating interactions increase and notes inconsistent moderation policies across geographical regions. It proposes automation supported by human review, but does not provide evidence on how the model performs in operational settings.

Methods

The paper presents a conceptual game-theoretic model of a romance scam using the minimax-regret criterion. It defines victim and scammer actions in a simplified $100 payment-request scenario, constructs a normal-form payoff matrix, converts entries into regret values, and identifies a minimax-regret value associated with selected actions.

Limitations

The work is an illustrative mathematical overview based on a single simplified scenario. It reports no empirical dataset, implementation, validation experiment, comparison with alternative detectors, or measured detection accuracy. Its payoff values and assumptions about victim and scammer behavior are presented without supporting empirical justification.

Future Work

Future research should empirically test the proposed minimax-regret model on real dating-platform interactions, assess false-positive and false-negative outcomes under human review, and compare it with existing moderation approaches.

See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.