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Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking

Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking

Jessica Whitney, Marisa Hultgren, Murray Eugene Jennex, Aaron Elkins, Eric Frost
Copyright: © 2020 |Pages: 30
ISBN13: 9781799823551|ISBN10: 1799823555|ISBN13 Softcover: 9781799823568|EISBN13: 9781799823575
DOI: 10.4018/978-1-7998-2355-1.ch014
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MLA

Whitney, Jessica, et al. "Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking." Knowledge Management, Innovation, and Entrepreneurship in a Changing World, edited by Murray Eugene Jennex, IGI Global, 2020, pp. 360-389. https://doi.org/10.4018/978-1-7998-2355-1.ch014

APA

Whitney, J., Hultgren, M., Jennex, M. E., Elkins, A., & Frost, E. (2020). Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking. In M. Jennex (Ed.), Knowledge Management, Innovation, and Entrepreneurship in a Changing World (pp. 360-389). IGI Global. https://doi.org/10.4018/978-1-7998-2355-1.ch014

Chicago

Whitney, Jessica, et al. "Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking." In Knowledge Management, Innovation, and Entrepreneurship in a Changing World, edited by Murray Eugene Jennex, 360-389. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2355-1.ch014

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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. Authors used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. Authors used the prototype to analyze a dataset generated from online ads from southern California and used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. An unexpected outcome of the second dataset was the discovery of the use of emojis in an expanded ontology.

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