An Entity Resolution approach to isolate instances of Human Trafficking online

TL;DR

This paper presents an entity resolution pipeline that leverages proxy labels to identify and cluster online instances of human trafficking, addressing challenges of data heterogeneity and noise in law enforcement efforts.

Contribution

The paper introduces a novel entity resolution approach using proxy labels to extract trafficking-related clusters from large, noisy online data sources.

Findings

Successfully applied to 5 million records from backpage.com

Identified domain-specific characteristics of trafficking entities

Discussed scalability challenges and performance metrics

Abstract

Human trafficking is a challenging law enforcement problem, and a large amount of such activity manifests itself on various online forums. Given the large, heterogeneous and noisy structure of this data, building models to predict instances of trafficking is an even more convolved a task. In this paper we propose and entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities.