Cracking Sex Trafficking: Data Analysis, Pattern Recognition, and Path Prediction

Abstract

Human trafficking, the exploitation of humans for monetary gain or benefit, is a widespread humanitarian issue that is typically sub‐classified into labor and sex trafficking. In the last decade, sex traffickers have used online classified advertisements to advertise sexual services. Although these advertisements are visible to the general public and law enforcement, the volume of ads, the frequency with which their posting locale changes, and the use of obfuscation tactics make it difficult for law enforcement agencies to react. Existing products for law enforcement focus on identifying, tracking, and correlating individual activity by performing deep searches for specific information against a database of historical posts. While this deep search capability is useful for investigating specific cases, it overlooks higher‐level patterns that exist in ads. Using a website that has been linked to several sex trafficking‐related arrests, we demonstrate a framework for harvesting, linking, and detecting these patterns in a dataset comprised of more than 10 million advertisements targeting US cities. Our framework combines information systems and operations research concepts to identify groups of posts based on text, phone numbers, and pictures; determine circuits associated with post groups, and predict future movements using four different methods. Our description of the framework and comparison of the grouping and prediction methods provide insights that can assist law enforcement agencies to combat individuals/organizations involved in illicit sexual activities, including sex trafficking, proactively. Also, this demonstration provides researchers interested in developing advanced interdiction models targeting illicit sexual activities with a clear picture regarding available data formats.