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

Author

Listed:
  • Nickolas Freeman

    (Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, Alabama 35478)

  • Greg Bott

    (Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, Alabama 35478)

  • Burcu Keskin

    (Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, Alabama 35478)

  • Jason Parton

    (Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, Alabama 35478)

  • James Cochran

    (Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, Alabama 35478)

Abstract

Problem definition : 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 U.S. 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. Methodology/results : 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. Managerial implications : 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.

Suggested Citation

  • Nickolas Freeman & Greg Bott & Burcu Keskin & Jason Parton & James Cochran, 2026. "Linking Multisite Sex Ad Data at the Individual Level to Aid Counter-Trafficking Efforts," Manufacturing & Service Operations Management, INFORMS, vol. 28(1), pages 133-152, January.
  • Handle: RePEc:inm:ormsom:v:28:y:2026:i:1:p:133-152
    DOI: 10.1287/msom.2024.0816
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    References listed on IDEAS

    as
    1. Burcu B. Keskin & Gregory J. Bott & Nickolas K. Freeman, 2021. "Cracking Sex Trafficking: Data Analysis, Pattern Recognition, and Path Prediction," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 1110-1135, April.
    2. Nickolas K. Freeman & Burcu B. Keskin & Gregory J. Bott, 2022. "Collaborating with Local and Federal Law Enforcement for Disrupting Sex Trafficking Networks," Interfaces, INFORMS, vol. 52(5), pages 446-459, September.
    3. Helen Shuxuan Zeng & Brett Danaher & Michael D. Smith, 2022. "Internet Governance Through Site Shutdowns: The Impact of Shutting Down Two Major Commercial Sex Advertising Sites," Management Science, INFORMS, vol. 68(11), pages 8234-8248, November.
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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