A Non-Parametric Learning Approach to Identify Online Human Trafficking
This addresses the law enforcement challenge of detecting human trafficking online, but it is incremental as it applies existing methods to a new domain with limited labeled data.
The study tackled the problem of identifying online human trafficking advertisements by analyzing data from Backpage, using a semi-supervised learning approach trained on hand-labeled and unlabeled data, and achieved evaluation with expert verification on unseen data.
Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website "Backpage"-- used for classified advertisement-- to discern potential patterns of human trafficking activities which manifest online and identify most likely trafficking related advertisements. Due to the lack of ground truth, we rely on two human analysts --one human trafficking victim survivor and one from law enforcement, for hand-labeling the small portion of the crawled data. We then present a semi-supervised learning approach that is trained on the available labeled and unlabeled data and evaluated on unseen data with further verification of experts.