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The cyber-industrialization of catfishing and romance fraud

Wang, Fangzhou ; Topalli, Volkan (2024) — Computers in Human Behavior

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DOI: 10.1016/j.chb.2023.108133 (opens in a new tab)

Type:   Journal Article
Country:   United States
Tags:   AI-Enabled Fraud, Algorithmic Management, Catfishing, Chat Moderators, Industrialized Fraud, Online Deception, Platform Policy, Romance Scams, Victim Experience, Worker Exploitation
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Synopsis

            
The article investigates a contemporary variant of online fraud that builds on romance scams and catfishing but is distinguished by its industrialized form, enabled by corporate practices, software platforms, and service workflows. The authors conduct an inductive analysis of publicly available testimonials and reviews provided by current and former employees connected to a particular contractor operating in the online customer service sector. In this arrangement, firms recruit individuals to act as chat moderators or general customer service agents with the instruction of promoting engagement across social channels. In practice, these workers are recruited to perform intimate texting tasks, compensated on a per-message basis, with clients led to believe they are interacting with female participants on a dating site. The operational framework is governed by client management systems that monitor worker productivity and monetize every exchange between clients and workers. The enterprise pursues efficiency by programmatically assigning multiple workers to single clients and by assembling background profiles on clients in real time, creating a tightly coordinated and scalable workflow. This mode of operation is described as an industrialized, corporatized form of fraud, characterized by procedural rigor and performance metrics rather than isolated incidents. The researchers refer to this practice as Intimacy Manipulated Fraud Industrialization (IMFI). They conclude that workers occupy dual roles, acting as both exploiters of clients and victims of the corporate structure that employs them. The study highlights how organized service platforms can transform exploitative intimate interactions into scalable business processes, raising questions about accountability, worker vulnerability, and the broader ethics of monetized online intimacy.

          

        
                  

            
Identified Gaps

            
IMFI has received little academic study and lacks a comprehensive socio-legal assessment or legal precedent. The available data do not directly capture clients’ experiences, reporting, losses, or repeat engagement. The authors also identify limited knowledge of worker experiences across companies and of how AI may automate personalized fraudulent intimacy at scale.

          

        
                  

            
Methods

            
The study analyzed publicly available narratives from current or former Cloudworkers employees and applicants. Researchers identified Cloudworkers through web searches, collected 54 reviews from Glassdoor and Trustpilot and 15 Reddit discussion posts, and used NVivo 12.0 for thematic coding. Both authors reviewed quotations and coded recurring themes, applying an existing romance-fraud framework of impression management, interpersonal deception, and social engineering to develop an IMFI process model.

          

        
                  

            
Limitations

            
The study relies on self-selected, open-source reviews and forum posts rather than interviews, offering only a limited view of one company. Testimonial authenticity cannot be verified; competitors or disgruntled former workers could post negative content, and reviewers may emphasize negative experiences. The study lacks direct client data and cannot determine whether IMFI is legally fraud, deviance, or exploitation because relevant legal precedents and comprehensive socio-legal assessment are absent.

          

        
                  

            
Future Work

            
Conduct direct interviews with workers across multiple chat-moderator companies and with clients to test, revise, or supplement the proposed model and to understand operations in real time. Examine legal classification and socio-legal responses to IMFI. Investigate AI-enhanced IMFI, including how large-language-model systems could automate personalized sexting interactions, and develop detection and prevention approaches.

          

              

    
    

        
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