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Tainted Love: a Systematic Literature Review of Online Romance Scam Research

Bilz, A. ; Shepherd, LA. ; Johnson, GI. (2023) — Interacting with Computers

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DOI: 10.1093/iwc/iwad048 (opens in a new tab)

Type:   Journal Article
Country:   United Kingdom
Tags:   AI-Enabled Fraud, Countermeasures, Cross Cultural, Law Enforcement, Machine Learning, Measurement, Offender Tactics, Platform Policy, Romance Scams, Systematic Review, Victim Experience, Victim Impacts, Victim Vulnerability
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Synopsis

            
Romance scams are orchestrated by online criminals who cultivate a deceptive romantic relationship on dating platforms with the aim of extracting money from victims. This form of cybercrime is harmful and often leaves those affected not only financially ruined but also emotionally distressed. The study surveys the scholarly landscape on romance scams to enhance understanding for researchers and practitioners by methodically examining and integrating both qualitative and quantitative evidence produced in recent years. The review identifies factors that influence victimhood and examines possible countermeasures to reduce the impact of these scams. A broad search covered ten scholarly databases and related websites using terms related to romance scams, and the process adhered to well-established guidelines for systematic reviews. From an initial pool of 279 papers screened, 107 were deemed eligible, and 53 were ultimately included in the final analysis. Three central contributions emerge: profiles and techniques commonly employed by romance scammers, the countermeasures available to mitigate these scams, and the factors that predispose individuals to become either a victim or a perpetrator. Although the body of literature on romance scams is growing, the number of empirical or experimental studies remains limited. The review highlights recurring patterns in how scammers operate and how victims may be drawn into the schemes, while also outlining practical measures designed to curb incidents. The authors conclude with directions for future research and with intervention strategies aimed at addressing victimhood. These strategies are intended for practitioners, law enforcement, and industry stakeholders who confront romance scams in various professional contexts. Overall, the work advances comprehension of romance scams by consolidating diverse strands of evidence and clarifying the elements that shape vulnerability and exposure to manipulation. By identifying common scam features, workable countermeasures, and risk factors linked to both victims and offenders, the study provides a structured foundation for ongoing scholarship and practical action. The concluding recommendations emphasize continued inquiry into victim support and intervention, as well as coordinated efforts across professional domains to deter fraudulent schemes and assist those affected.

          

        
                  

            
Identified Gaps

            
Empirical and experimental studies remain limited. Research on victim–scammer messaging interactions, perpetrators’ demographics and psychology, and post-scam recovery is scarce. Available data are often under-reported, incomplete, reused across studies, or inaccessible because of ethical, legal, and institutional barriers. Findings on victim risk factors are sometimes contradictory, while technical detection studies lack common datasets and practical deployment.

          

        
                  

            
Methods

            
PRISMA-guided systematic literature review. The authors searched 10 scholarly databases plus reference lists and Google Scholar alerts, initially identifying 334 records. After deduplication and staged title/abstract and full-text screening, 53 English-language empirical or experimental studies were included. Extracted metadata and findings were double-validated in Covidence, then synthesized descriptively because study designs were too heterogeneous for advanced statistical processing.

          

        
                  

            
Limitations

            
The review was limited to English-language, accessible literature and excluded studies with unclear or non-empirical methodologies. Included research was methodologically heterogeneous, limiting direct comparison. The underlying literature often relied on under-reported victim data, small samples, incomplete disclosure of data sources, and repeated use of a few public datasets. These constraints limit generalizability, especially for machine-learning detection findings.

          

        
                  

            
Future Work

            
Develop flexible scam models that include recovery and evolving technologies. Conduct region-specific research and disaggregate data to identify vulnerability patterns by scam type. Build standardized, machine-readable datasets and usable end-user detection tools through collaboration among researchers, platforms, and law enforcement. Develop interactive awareness training, victim support, and ethically governed cross-platform research.

          

              

    
    

        
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