# Semi-Supervised Learning for Detecting Human Trafficking

## TL;DR

This paper introduces a semi-supervised learning approach using Laplacian SVM variants to detect potential human trafficking advertisements online, leveraging limited labeled data and unlabeled web content.

## Contribution

We develop S3VM-R, an enhanced semi-supervised learning method that incorporates exogenous information for better detection of trafficking-related ads.

## Key findings

- S3VM-R outperforms existing semi-supervised methods in identifying relevant ads.
- The approach effectively utilizes limited labeled data with abundant unlabeled data.
- Law enforcement experts verified the high-interest advertisements identified by our method.

## Abstract

Human trafficking is one of the most atrocious crimes and among the challenging problems facing law enforcement which demands attention of global magnitude. In this study, we leverage textual data from the website "Backpage"- used for classified advertisement- to discern potential patterns of human trafficking activities which manifest online and identify advertisements of high interest to law enforcement. Due to the lack of ground truth, we rely on a human analyst from law enforcement, for hand-labeling a small portion of the crawled data. We extend the existing Laplacian SVM and present S3VM-R, by adding a regularization term to exploit exogenous information embedded in our feature space in favor of the task at hand. We train the proposed method using labeled and unlabeled data and evaluate it on a fraction of the unlabeled data, herein referred to as unseen data, with our expert's further verification. Results from comparisons between our method and other semi-supervised and supervised approaches on the labeled data demonstrate that our learner is effective in identifying advertisements of high interest to law enforcement