Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning
31 Pages Posted: 18 Jun 2021 Last revised: 4 Aug 2022
Date Written: June 13, 2021
Abstract
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. We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework to unmask suspicious recruitment-to-sales pathways, thereby providing the first global network view of trafficking risk in commercial sex supply chains. This allows us to infer likely recruitment-to-sales trafficking routes of criminal entities, deceptive approaches used to recruit victims, and regional variations in recruitment vs. sales pressure. These insights can help law enforcement agencies along trafficking routes better coordinate efforts, as well as target local counter-trafficking policies and interventions towards exploitative behavior frequently exhibited in that region.
Keywords: sex trafficking, commercial sex supply chains, recruitment, deep web, machine learning
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