Narrative to Trajectory (N2T+): Extracting Routes of Life or Death from Human Trafficking Text Corpora

TL;DR

This paper introduces N2T+, a system that uses NLP and data science to extract and map trafficking routes from survivor narratives, aiding anti-trafficking efforts.

Contribution

N2T+ is a novel multi-dimensional approach that significantly improves geolocation detection from trafficking narratives compared to existing methods.

Findings

Higher geolocation accuracy than state-of-the-art techniques

Effective extraction of trafficking routes from textual narratives

Enhanced mapping of routes for anti-trafficking interventions

Abstract

Climate change and political unrest in certain regions of the world are imposing extreme hardship on many communities and are forcing millions of vulnerable populations to abandon their homelands and seek refuge in safer lands. As international laws are not fully set to deal with the migration crisis, people are relying on networks of exploiting smugglers to escape the devastation in order to live in stability. During the smuggling journey, migrants can become victims of human trafficking if they fail to pay the smuggler and may be forced into coerced labor. Government agencies and anti-trafficking organizations try to identify the trafficking routes based on stories of survivors in order to gain knowledge and help prevent such crimes. In this paper, we propose a system called Narrative to Trajectory (N2T+), which extracts trajectories of trafficking routes. N2T+ uses Data Science and Natural Language Processing techniques to analyze trafficking narratives, automatically extract relevant location names, disambiguate possible name ambiguities, and plot the trafficking route on a map. In a comparative evaluation we show that the proposed multi-dimensional approach offers significantly higher geolocation detection than other state of the art techniques.