Skip to main content

Love as Bait: A Scoping Review and Crime Script Analysis of Online Romance Scams

Schokkenbroek, JM. ; Snaphaan, T. (2025) — Trauma, Violence, & Abuse

View Original Source (opens in a new tab)

DOI: 10.1177/15248380251361046 (opens in a new tab)

Type:   Journal Article
Country:   Netherlands
Tags:   AI-Enabled Fraud, Crime Script Analysis, Deepfakes, Grooming, Modus Operandi, Offender Tactics, Platform Policy, Prevention, Revictimization, Romance Scams, Scoping Review, Sextortion, Victim Experience
Citation tools
Download RIS Download BibTeX 


              
              

            

          

        


      

      

    

        
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
                

            

                    

                
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:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
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 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

            
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

            
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

            
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

            
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.

          

              

    
    

        
Related Research Insights

        

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