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

Marguerite DeLiema ; Paul Witt (2023) — Security Journal

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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
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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.

          

              

    
    

        
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