Skip to main content

Profiling consumers who reported mass marketing scams: demographic characteristics and emotional sentiments associated with victimization

DeLiema, Marguerite ; Witt, Paul (2023) — Security Journal

View Original Source (opens in a new tab)

DOI: 10.1057/s41284-023-00401-5 (opens in a new tab)

Type:   Journal Article
Country:   United States
Tags:   AI-Enabled Fraud, Consumer Complaints, Financial Fraud, Ftc Consumer Sentinel, Mass Marketing Scams, Risk Factors, Romance Scams, Sentiment Analysis, Victim Experience, Victim Profiling
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

            
The study analyzed 1,010,748 FTC Consumer Sentinel complaints filed in the United States from November 2020 to April 2022. It compared reported financial-loss victimization ($1+) with attempted fraud across romance, tech-support, prize/lottery, government-imposter, business-imposter, and online-shopping scams. Social media was the most common reported contact method for romance scams, accounting for 37.3% of complaints. Romance-scam victims aged 70 and older reported median losses of $10,000, compared with $450 to $3,000 among reporters younger than 50. Consumer complaints are not representative of underlying victimization: reporting propensity, exposure, loss size, education, and trust in authorities may vary by group and scam type.

          

        
                  

            
Identified Gaps

            
Complaint data cannot disentangle scam exposure, actual victimization, and propensity to report. The authors identify a need to clarify whether demographic differences reflect exposure, contextual risk, or reporting motivations and barriers. They also note uncertainty over whether complaint sentiment reflects emotions during victimization or emotional aftermath. Underrepresentation is likely among people with developmental disability, dementia, extreme poverty, or homelessness.

          

        
                  

            
Methods

            
The study analyzed 1,010,748 FTC Consumer Sentinel complaints filed in the United States from November 2020 to April 2022. It compared reported financial-loss victimization ($1+) with attempted fraud across romance, tech-support, prize/lottery, government-imposter, business-imposter, and online-shopping scams. Separate logistic regressions included age, algorithmically estimated sex and race/ethnicity, contact method, season, proxy reporting, ZIP-code complaint rate, narrative length, and VADER-derived sentiment valence.

          

        
                  

            
Limitations

            
Consumer complaints are not representative of underlying victimization: reporting propensity, exposure, loss size, education, and trust in authorities may vary by group and scam type. Scam categories are self-selected and unverified. Sex and race/ethnicity were estimated rather than self-reported; about one-third could not be assigned sex, and BIFSG is less accurate for American Indian/Alaska Native and multiracial people. VADER was developed for tweets, and all cases occurred during COVID-19.

          

        
                  

            
Future Work

            
Future work should assess whether tech-support and romance-scam victims report to law enforcement at higher rates because their losses are relatively high. It should test alternative AI-powered sentiment tools, use qualitative research to distinguish emotions during the scam from post-incident reactions, and conduct scalable qualitative analyses to identify consumers needing victim-support services.

          

              

    
    

        
Related Research Insights

        

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