A Non-Parametric Learning Approach to Identify Online Human Trafficking

TL;DR

This paper introduces a semi-supervised learning method to detect online human trafficking advertisements using data from classified ads, leveraging expert-labeled samples to identify potential trafficking patterns.

Contribution

It presents a novel semi-supervised approach that combines limited expert-labeled data with unlabeled data to identify trafficking-related online ads.

Findings

Effective identification of trafficking ads from online data

Utilization of expert-labeled samples improves detection accuracy

Potential to assist law enforcement in combating trafficking

Abstract

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.