Linking Multi-Site Sex Ad Data at the Individual Level to Aid Counter-Trafficking Efforts

TL;DR

This paper introduces an efficient, multi-site linking process for sex ad data that enhances counter-trafficking efforts by accurately identifying potential victims and enabling faster data processing across numerous online platforms.

Contribution

The paper presents a novel, scalable method combining network science and AI techniques to link sex ads across multiple sites, improving data accuracy and processing speed for counter-trafficking intelligence.

Findings

Successfully linked over 60 potential victims.

Processed millions of ads in under an hour.

Outperformed existing approaches in efficiency and data linkage accuracy.

Abstract

The Internet facilitates sex trafficking through adult service websites (ASWs) that host online advertisements for sexual services (sex ads). Since the closure of the popular site Backpage.com, the ecosystem of ASWs has expanded to include multiple competing sites that are hosted outside US jurisdiction. Gaining intelligence for counter-trafficking efforts requires collecting, linking, and cleaning the data from multiple sites. However, high ad volumes, disparate data types, and the existence of generic and misappropriated data make this process challenging. We present an end-to-end process for linking sex ad data and filtering potentially erroneous links. Outputs of the developed process have been used to inform counter-trafficking operations that have helped identify more than 60 potential victims of sex trafficking, some of whom are getting help to transition out of the life. Our process leverages concepts and techniques from network science, information systems, and artificial intelligence to link ads across sites at the level of an individual or unique posting entity. Our approach is computationally efficient, allowing millions of ads to be processed in under an hour. A key component of our process is an edge filtering procedure that identifies and removes potentially erroneous links in a graph representation of sex ad data. A comparison of the proposed process to an existing approach shows that our process is typically more computationally efficient and yields substantial increases in the number of individuals for which we can derive actionable intelligence. The proposed process is an efficient and effective approach for transforming the high volumes of disparate data from sex ads into intelligence that can save lives. It has been refined over years of collaboration with practitioners and represents a strong foundation upon which further counter-trafficking tools can be built.