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Victim-offender overlap: the identity transformations experienced by trafficked Chinese workers escaping from pig-butchering scam syndicate

Wang, Fangzhou (2024) — Trends in Organized Crime

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DOI: 10.1007/s12117-024-09525-5 (opens in a new tab)

Type:   Journal Article
Country:   United States
Tags:   AI-Enabled Fraud, Coercive Control, Forced Criminality, Identity Transformation, Labor Trafficking, Offender Tactics, Pig Butchering, Platform Policy, Prevention, Romance Scams, Social Engineering, Trafficked Workers, Victim Experience, Victim Offender Overlap
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Synopsis

            
This study explores the intersection of human trafficking and online romance fraud within the context of the pig-butchering scam (Sha Zhu Pan, 杀猪盘). While the scam is widely recognized for combining romance and investment fraud through persuasive social engineering, less is known about its reliance on trafficked Chinese workers who are coerced into perpetrating scams. The paper examines how these workers undergo identity transformations, occupying the dual roles of victim and offender. Drawing on 36 victim testimonials published on Zhihu and WeChat Official Accounts, the study employs thematic analysis to map a four-stage model of transformation: Onset (deceptive entry): Workers are lured by fake job ads or recruitment through acquaintances, often promised high salaries and comfortable living conditions. Some are deceived via legitimate-seeming career fairs. Acculturation (from victims to scammers): Upon arrival in northern Myanmar, workers are subjected to isolation, financial coercion, physical abuse, drug influence, and psychological manipulation. These methods force compliance and push individuals into scamming roles. Turning point: Workers are assessed based on their ability to generate profits. Those failing to meet targets are often resold to other scam operations, treated as commodities. Repeated reselling deepens their entrapment and erodes resistance, while some may transform into willing offenders for financial survival. Escape or continued bondage: A minority escape through luck, rescue operations, or resilience. Survivors often experience shame, regret, and lingering trauma, while others remain trapped, coerced into fraudulent activities indefinitely. The findings highlight forced criminality as a distinctive form of trafficking, where victims are not only exploited for labor but compelled to commit crimes against others. This victim-offender overlap blurs legal and moral boundaries: trafficked workers recognize their coercion yet bear guilt for defrauding others. Many surrender voluntarily to police upon return, while some channel their experiences into awareness and rescue efforts. The study’s contributions are threefold. First, it extends the victim-offender overlap framework to cyberscam trafficking, a domain rarely studied in international contexts. Second, it underscores the industrialized structure of pig-butchering scams, involving recruitment, IT, and money-laundering divisions. Third, it raises urgent policy implications. Governments must strengthen cross-border cooperation in Myanmar to combat trafficking, refine legal definitions of coercion in Chinese law to account for deceptive recruitment and violence, and provide reintegration support for rescued workers. Finally, the article warns of technological risks. With the rise of AI tools such as chatbots and deepfakes, syndicates may reduce reliance on human trafficked labor, making detection even harder. Addressing pig-butchering scams therefore requires not only anti-fraud strategies but also coordinated responses to human trafficking and technological abuse.

          

        
                  

            
Identified Gaps

            
The paper identifies limited research on forced labor trafficking into online romance scams and on victim-offender overlap in cybercrime, particularly in non-Western and international settings. It notes an absence of empirical study of harm-inflicting forced labor trafficking, where trafficked people are compelled to defraud others. Knowledge is particularly lacking about workers who meet scam performance expectations, remain with syndicates, or may shift voluntarily toward offender identities.

          

        
                  

            
Methods

            
The study qualitatively analyzed 36 Chinese-language testimonials from Zhihu (n=16) and WeChat Official Accounts (n=20), concerning mainland Chinese citizens trafficked into scam operations. Posts were screened for recency, detail, personal narrative, and credibility; WeChat accounts were cross-checked against official government news sources. The researcher translated testimonials into English, conducted thematic analysis in NVivo using two separate 18-story projects, reconciled themes, and developed a four-stage identity-transformation model.

          

        
                  

            
Limitations

            
The sample consists mainly of self-disclosures and media-derived accounts from escapees, while many trafficked workers remain unrescued or fear retaliation; findings therefore have limited generalizability. Zhihu posts could potentially have been produced by scammers, although the author considers this unlikely. Testimonial accounts may also be affected by fragmented or repressed memories after traumatic violence. The absence of direct interviews and accounts from workers who remained in compounds limits validation of the proposed model.

          

        
                  

            
Future Work

            
Future research should combine open-source testimonials with direct interviews or surveys involving trafficked workers, family members, law-enforcement personnel, and relevant social organizations to validate and extend the model. It should specifically examine workers who meet scam performance criteria, gain syndicate trust, or remain within scam compounds. Research should also examine escape-oriented mindsets, trafficking control techniques across male and female victims, and how these dynamics shape desistance from offender identities.

          

              

    
    

        
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