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Investigating Online Dating Fraud: An Extensive Review and Analysis

Smita Bharne ; Pawan Bhaladhare (2022) — 2022 International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC)

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DOI: 10.1109/icmacc54824.2022.10093271 (opens in a new tab)

Country:   Global
Tags:   Fake Profiles, Machine Learning, Online Dating Platforms, Prevention, Romance Scams, Scammer Detection, Victim Impacts
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Synopsis

            
This publication provides an extensive review of online dating fraud (ODF), also known as romance scams, to document how scammers create false identities on dating sites and social networks to deceive victims financially and emotionally. The authors describe ODF as a form of mass marketing fraud in which offenders cultivate a relationship with a victim, often using fake profiles, grooming, and repeated pressure to extract money or sensitive information. The work emphasizes the global reach of ODF, noting that billions use online dating platforms and that the United States, Europe, and other regions represent major markets for both users and reported cases. The paper outlines the stages of typical scams, from profile creation and initial contact to grooming, solicitation of money, crisis-driven requests, sexual exploitation, and eventual re-victimization. The article surveys the landscape of dating platform features, site types, and the diverse tactics scammers employ. It highlights the emotional and financial harms to victims, including depression, reputational damage, and in some cases more severe outcomes. The authors summarize existing prevention and awareness efforts, as well as data-driven, machine-learning–based approaches aimed at detecting scammers. They categorize detection strategies by the type of signal used—behavioral patterns, profile and image analysis, and text/content analysis—and discuss how each modality faces limitations given the evolving sophistication of fraudsters. The review also covers data collection and methodological challenges, such as the reliance on restricted datasets and private conversations, which complicate validation and real-time deployment. Finally, the publication identifies open research questions and areas for future work, including the need for scalable, privacy-preserving detection methods and improved user safety guidance. It stresses that while progress in ML-based detection is promising, there is no one-size-fits-all solution, and a holistic approach combining prevention, awareness, and scalable detection is required to mitigate ODF.

          

        
                  

            
Identified Gaps

            
The review identifies limited scholarship on romance-scam prevention, awareness, impacts, and technical data-driven detection. It reports a lack of effective prevention metrics and best-practice models. Dating-fraud detection research is constrained by scarce real-world datasets, unavailable private chats, and absent legal or ethical guidance for research using such data. Existing machine-learning studies are limited and often tested on few dating websites or small datasets.

          

        
                  

            
Methods

            
This narrative review synthesizes prior literature on online dating fraud, including scam stages, victim impacts, prevention and awareness approaches, dating-site features, and detection taxonomies. It summarizes published machine-learning approaches using profile, image, text, behavioral, and IP-address-related signals, including SVMs, decision trees, deep neural networks, reverse image search, and face-recognition APIs. The paper presents comparative tables of prevention studies, data-driven detection studies, and detection features.

          

        
                  

            
Limitations

            
The paper is a broad review rather than an original empirical evaluation. It reports that prior detection studies rely on limited datasets or a single/few dating sites, while private peer-to-peer chats are often unavailable for investigation. The review also notes that conventional spam and fake-profile methods are poorly suited to dating contexts because behaviors such as contacting strangers and providing inaccurate profile attributes can be normal on dating sites.

          

        
                  

            
Future Work

            
Develop early-stage, effective scammer-detection systems, potentially enhanced with artificial intelligence. Build and ethically govern access to real-world and real-time dating-fraud datasets. Evaluate detection methods that combine image and text features, behavioral signals, and machine learning under the distinctive conditions of dating platforms. Develop and test best-practice prevention models and metrics for proactive awareness interventions.

          

              

    
    

        
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