Love as Bait: A Scoping Review and Crime Script Analysis of Online Romance Scams
Schokkenbroek, JM. ; Snaphaan, T. (2025) — Trauma, Violence, & Abuse
Type:
Journal Article
Country:
Netherlands
Synopsis (AI-Generated)
This scoping review searched Web of Science and Scopus on November 13, 2024, then supplemented those results with reference lists and Google Scholar. Fifty empirical studies describing scammer behavior were included from 318 initial records. Crime-script analysis organized the literature into nine scenes covering preparation, target selection, first contact, relationship and trust development, financial exploitation, possible sextortion, continuation, and aftermath. The resulting script offers a structured map for locating intervention points and comparing offender actions across studies; it is a synthesis, not a claim that every scam follows one fixed sequence. Its completeness depends on searchable, included research, so emerging tactics, unpublished cases, languages, and under-studied settings may be absent.
Identified Gaps (AI-Generated)
Prior models lack an integrated, detailed overview of scammer modus operandi. They often omit information gathering, revictimization, scam endings, and links to identity theft, laundering, and sextortion; differ in perspectives, organizational focus, and detail; and commonly assume linearity. Evidence is geographically and demographically skewed toward the United States, Australia, and United Kingdom, with limited research on other regions, marginalized populations, LGBTQIA+ victims, and non-mainstream platforms. Direct offender data and empirical validation of the resulting script are also scarce.
Methods (AI-Generated)
The authors conducted a scoping review of Web of Science and Scopus searches on November 13, 2024, supplemented by reference-list and Google Scholar searches. From 318 initial records, 50 empirical studies describing scammer behavior were included. Ten existing process models were descriptively compared. Across all 50 studies, passages on strategies and process models were annotated and coded through a deductive preliminary framework plus iterative inductive refinement. The resulting crime script organized scammer behavior into nine scenes and alternative actions.
Limitations (AI-Generated)
The script depends on the quality and scope of included research, so strategies outside those studies may be missed. Most source studies relied on victims, experts, legal records, or limited ethnography rather than direct offender accounts, constraining insight into offender decisions and action sequences. Included evidence is geographically and demographically skewed, limiting representation of diverse contexts and populations. The script has not been empirically validated and may become outdated as scammers adopt AI, deepfakes, automation, and other technologies.
Future Work (AI-Generated)
Validate the crime script across contexts using court records and interviews or surveys with offenders, victims, practitioners, support organizations, and law enforcement. Obtain ethically rigorous direct offender perspectives through ethnography, communication analysis, or case files. Use inclusive, intersectional research on underexamined regions and groups, including LGBTQIA+ people. Monitor and regularly revise the script for AI, deepfake, automation, and other emerging tactics; apply financial crime scripting to examine business models and money laundering.
AI-Generated Content Notice
The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.
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