24 Scopus citations

Abstract

Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website 'Backpage'-used for classified advertisement-to discern potential patterns of human trafficking activities which manifest online and identify most likely trafficking related advertisements. Due to the lack of ground truth, we rely on two human analysts-one human trafficking victim survivor and one from law enforcement, for hand-labeling the small portion of the crawled data. We then present a semi-supervised learning approach that is trained on the available labeled and unlabeled data and evaluated on unseen data with further verification of experts.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligence and Security Informatics: Cybersecurity and Big Data, ISI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages133-138
Number of pages6
ISBN (Electronic)9781509038657
DOIs
StatePublished - Nov 15 2016
Event14th IEEE International Conference on Intelligence and Security Informatics, ISI 2015 - Tucson, United States
Duration: Sep 28 2016Sep 30 2016

Other

Other14th IEEE International Conference on Intelligence and Security Informatics, ISI 2015
CountryUnited States
CityTucson
Period9/28/169/30/16

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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    Alvari, H., Shakarian, P., & Snyder, J. E. K. (2016). A non-parametric learning approach to identify online human trafficking. In IEEE International Conference on Intelligence and Security Informatics: Cybersecurity and Big Data, ISI 2016 (pp. 133-138). [7745456] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2016.7745456