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Identifying Victims of Human Sex Trafficking in Online Ads
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Author(s): Jessica Whitney (San Diego State University, USA), Marisa Hultgren (San Diego State University, USA), Murray Eugene Jennex (San Diego State University, USA), Aaron Elkins (San Diego State University, USA)and Eric Frost (San Diego State University, USA)
Copyright: 2020
Pages: 21
Source title:
Encyclopedia of Criminal Activities and the Deep Web
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-9715-5.ch034
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Abstract
Social media and the interactive web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. A prototype was designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management to generate actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. The prototype was used to analyze data sets generated from online ads. An unexpected outcome of the second data set was the discovery of the use of emojis in an expanded ontology. The final prototype used the expanded ontology to identify potential victims. The results of applying the prototypes suggest a viable approach to identifying victims of human sex trafficking in online ads.
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