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“My Agent Will Not Let Me Talk to the General”: Software Agents as a Tool Against Internet Scams

Alexander Norta ; Katrin Nyman-Metcalf ; Anis Ben Othman ; Addi Rull (2016) — The Future of Law and eTechnologies

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DOI: 10.1007/978-3-319-26896-5_2 (opens in a new tab)

Country:   Global
Tags:   Machine Learning, Online Trust, Platform Responsibility, Romance Scams, Scam Detection, Software Agents, Warning Flags
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Synopsis

            
The publication examines how technology can help users navigate the risks of online interaction, using a romance-scam case to illustrate why trust signals are harder to read online and why standard social cues falter in cyberspace. Its purpose is to explore whether software agents can act as guardians—raising red flags and supporting vigilant behavior for users who may not detect danger themselves. The authors argue that, given jurisdictional and privacy challenges in cyberspace, well-designed agent-enabled tools could complement law and regulation by providing preventative assistance rather than reacting after harm occurs. The chapter describes a setting based on a ten-day romance-scam scenario initiated on a professional network, highlighting warning signals such as incompatible communication patterns, unusual profile behavior, and inconsistencies that emerge as interactions unfold. It reviews relevant legal and data-protection contexts and draws on routine-activities theory to frame the idea that intelligent agents could serve as “guardians” in online environments. A proposed architecture includes multiple agent types: communication listeners that apply linguistic analysis to flag indecent or risky content, pattern detectors that identify telltale behaviors (such as declined personal meetings or requests for money), and a risk-benefit evaluator that can warn users and surface policy or privacy concerns. The authors acknowledge limitations and practical tensions, noting enforcement difficulties across borders and the need for machine-readable privacy policies, human configuration, and careful consideration of data handling. They advocate for self-regulation by platforms and the deployment of a meta-level, facade-like platform to enhance user privacy and security, rather than relying solely on traditional law enforcement. The work emphasizes the potential utility of technology as a preventive tool, while also recognizing its design and governance challenges.

          

        
                  

            
Identified Gaps

            
The proposed pattern set covers only the started-interaction stage of the online-dating lifecycle. Patterns for profile establishment, criteria-based selection, interaction halts and resumption, in-person meetings, and scam completion or failure are missing. Detailed technical realizations of the patterns also remain unspecified.

          

        
                  

            
Methods

            
The chapter uses a qualitative case study of a 10-day attempted romance scam initiated on LinkedIn in November 2014. It extracts warning signs from the interaction, formalizes them as online-dating scam patterns, maps them to a dating-lifecycle taxonomy, and proposes an OWL-based metadata model and machine-learning multi-agent warning-system concept. It also analyzes legal, privacy, jurisdiction, and platform-responsibility issues.

          

        
                  

            
Limitations

            
The analysis is grounded primarily in one attempted scam involving an unusually vigilant, professionally knowledgeable intended victim. The authors acknowledge that the listed patterns cover only one lifecycle category and lack detailed technical realizations. Several claims about offender selection, gang involvement, and typical scam behavior are presented as probabilities or assumptions rather than verified observations.

          

        
                  

            
Future Work

            
Expand scam-pattern specifications across unexamined lifecycle stages; refine patterns into detailed technical implementations; determine agent activation, settings, functions, usability, governance, and incentives; and develop a configurable meta-platform with user-coordinated sub-agents.

          

              

    
    

        
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