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.Comment: Accepted in IEEE Intelligence and Security Informatics 2016
Conference (ISI 2016