Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning

Problem definition : The covert nature of sex trafficking provides a significant barrier to generating large-scale, data-driven insights to inform law enforcement, policy, and social work. Existing research has focused on analyzing commercial sex sales on the internet to capture scalable geographical proxies for trafficking. However, ads selling commercial sex do not reveal information about worker consent. Therefore, it is challenging to identify risk for trafficking, which involves fraud, coercion, or abuse. Methodology/results : We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework (combining natural language processing, active learning, and network analysis) to study how and where sex worker recruitment occurs. This allows us to unmask potentially deceptive recruitment patterns (e.g., an entity that recruits for modeling but sells sex), which signal high trafficking risk. We demonstrate via simulations that our approach outperforms existing active learning techniques to identify key nodes and edges in the underlying trafficking network. Our analysis provides a geographical network view of online commercial sex supply chains, highlighting deceptive recruitment-to-sales pathways that are likely trafficking routes. Managerial implications : Our results can help law enforcement agencies along trafficking routes better coordinate efforts to tackle trafficking entities at both ends of the supply chain, as well as target local social policies and interventions toward exploitative recruitment behavior frequently exhibited in that region.