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Ethical and Social Challenges with developing Automated Methods to Detect and Warn potential victims of Mass-marketing Fraud (MMF)

Monica Whitty ; Matthew Edwards ; Michael Levi ; Claudia Peersman ; Awais Rashid ; Angela Sasse ; Tom Sorell ; Gianluca Stringhini (2017) — Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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DOI: 10.1145/3041021.3053891 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   Automated Detection, Ethical AI, Machine Learning, Mass Marketing Fraud, Online Dating, Privacy, Romance Scams, Warning Systems
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Synopsis

            
The publication presents an interdisciplinary effort to develop automated methods for detecting mass-marketing fraud (MMF) and warning potential victims, with a focus on mass-market scams including romance scams. Its central aim is to prevent victimization by enabling early identification of deceptive communication and grooming strategies across multiple online channels. The authors describe an ongoing project that blends psychology, media studies, criminology, linguistics, and human-computer interaction to identify signals of deception, grooming, and persuasive requests within end-user communications and profiles. The paper outlines a research program that would use personal data from dating sites, employment pages, emails, and other sources to train and test machine-learning and pattern-recognition approaches. Proposed methods include supervised learning (e.g., random forests) and clustering to distinguish scam-related behavior and to uncover linguistic and stylistic indicators typical of victims and scammers. They also consider analyzing sociotechnical features such as response patterns, profile descriptions, and multi-channel interactions. Ethical and social challenges are given substantial attention: data anonymization, informed consent, potential false positives, and the need to balance protection with user autonomy. The authors acknowledge the difficulty of making automated judgments about authenticity and the risk of harming the very individuals they aim to help, suggesting human-in-the-loop or warning-message strategies as possible mitigations. The discussed implications emphasize that while automated MMF detection could reduce financial and psychological harm, it raises privacy concerns and governance questions about how such systems are deployed, how risks of misclassification are managed, and how user trust is maintained. The publication stops short of presenting empirical results, instead outlining the conceptual framework, methodological directions, and ethical constraints guiding future work.

          

        
                  

            
Identified Gaps

            
Existing scam education focused on idealized individual behavior and knowledge may not prevent victimization; awareness can coexist with vulnerability. MMF detection is difficult because offenders use personalized, adaptive, long-term communication across multiple channels. The paper also identifies an unresolved need to design detection and warning systems that balance prevention with privacy, autonomy, false-positive harms, and trust.

          

        
                  

            
Methods

            
This extended abstract describes early planning for an interdisciplinary MMF detection project. The proposed research analyzes dating and employment profiles and communications from victims, scammers, and noncriminal contacts. It plans to examine psychological, linguistic, behavioral, and socio-technical indicators, using anonymization, supervised machine learning (including random forests), and profile clustering. If detection proves effective, the team plans a browser-extension proof of concept and HCI-informed tests of warning messages.

          

        
                  

            
Limitations

            
The paper reports planned work rather than completed empirical findings, validation results, or performance measures. Detection may produce false positives that harm genuine users and relationships. The proposed system would require analysis of highly personal data and may make authenticity judgments without the other person's knowledge. Victims may also distrust or disregard warnings, limiting intervention effectiveness.

          

        
                  

            
Future Work

            
Develop and evaluate a proof-of-concept browser extension that detects suspected scam accounts and warns users. Test the effectiveness of alternative warning-message designs. Explore warning approaches that retain human judgment, such as guided authenticity checks, and consider whether tools should be situated on dating sites or support networks.

          

              

    
    

        
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