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.

Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond

“…It is rather well agreed that AI can help with detecting patterns (Keskin et al. 2020), supporting automatic and smart procurement (Cui et al. 2021) and achieving scientifically sound recommendations.…”

“…2 It is reported that 10% of enterprises nowadays are using AI in their business operations (Columbus 2020a). It is rather well agreed that AI can help with detecting patterns (Keskin et al 2020), supporting automatic and smart procurement (Cui et al 2021) and achieving scientifically sound recommendations. AI and robotics are so well-developed that they outperform humans in various areas (Geva and Saar-Tsechansky 2021).…”

Improving Access to Housing and Supportive Services for Runaway and Homeless Youth: Reducing Vulnerability to Human Trafficking in New York City

“…Research related to OR and analytics efforts to reduce homelessness and the supply of sex and labor trafficking victims has only recently received attention and largely focus on three broad areas: the scope of the problem (Kosmas et al, 2020;Brelsford and Parakh, 2018), frameworks for addressing the crisis (Taylor, 2018;Kővári and Pruyt, 2014), and appropriate allocation of scarce resources to combat HT and homelessness (Chan et al, 2018;Petry et al, 2021). A second body of OR literature focuses on detection of hidden HT victims (Keskin et al, 2021;Kapoor et al, 2017;Mensikova and Mattmann, 2017), movement patterns of covert traffickers (McDonald et al, 2021;Yao et al, 2021;Amin, 2010) and long-term intervention approaches to prevent trafficking or retrafficking by increasing the efficiency and effectiveness of public services (Amadasun, 2020), as well as improving access to supportive and rehabilitative services (Azizi et al, 2018). However, little attention has been paid to using OR techniques to examine the problem of allocating scarce resources for anti-trafficking efforts, with the exceptions of Konrad (2019) and Maass et al (2020).…”

A Transdisciplinary Approach for Generating Synthetic but Realistic Domestic Sex Trafficking Networks

“…Mayorga et al (Mayorga et al 2019) used web scraping from an online escort service to model the movement of sex trafficking networks between different cities. Keskin et al (Keskin et al 2021) proposed a process of grouping online advertisements to predict the movement of sex trafficking networks based on where related advertisements appear. White et al (White et al 2021) used web scraping to gather data on illicit massage parlors, and determines potential contributing factors for where illicit massage parlors may be located.…”

“…Since these networks are illicit in nature, their operations are hidden to avoid detection (Farrell & De Vries 2020, Fedina 2015, Konrad, Trapp & Maass 2017, Konrad, Trapp, Palmbach & Blom 2017. Current data for analytic approaches often comes from case files, scraped web data on sex advertisements, or data pertaining to massage parlors (Keskin et al 2021, Mayorga et al 2019, White et al 2021. This data can be viewed as the "forwardfacing" aspects of the operations of sex trafficking networks in terms of how they interact with the outside world, thus it is (somewhat) publicly accessible.…”