Creating Geospatial Trajectories from Human Trafficking Text Corpora

TL;DR

This paper introduces N2T, a system that extracts geospatial trajectories from human trafficking narratives using NLP and data augmentation, aiding in route identification for crime prevention.

Contribution

The paper presents a novel NLP-based system that automatically extracts and plots trafficking routes from textual reports, outperforming existing geolocation methods.

Findings

N2T achieves higher accuracy in geolocation extraction.

Data augmentation improves trajectory detection performance.

System effectively visualizes trafficking routes from narrative data.

Abstract

Human trafficking is a crime that affects the lives of millions of people across the globe. Traffickers exploit the victims through forced labor, involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a form of human trafficking when the migrant fails to pay the smuggler and is forced into coerced activities. Several news agencies and anti-trafficking organizations have reported trafficking survivor stories that include the names of locations visited along the trafficking route. Identifying such routes can provide knowledge that is essential to preventing such heinous crimes. In this paper we propose a Narrative to Trajectory (N2T) information extraction system that analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation in order to automatically plot trajectories of human trafficking routes. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods.