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
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利用知识管理和机器学习来识别人口贩运的受害者
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
E. Frost
中科院分区：
文献类型：
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作者：
Jessica Whitney;Marisa Hultgren;M. Jennex;Aaron C. Elkins;E. Frost
社交媒体和互动网络使人口贩运者能够引诱受害者，然后比以往任何时候都更快、更安全地将其出售。然而，这些工具也使调查人员能够寻找受害者和罪犯。作者使用系统开发行动研究方法来创建和应用一个原型，旨在通过分析在线广告来识别人口贩运的受害者。该原型使用知识管理方法，通过应用一组基于本体的强过滤器来识别潜在的受害者，从而生成可操作的情报。作者使用该原型分析了从南加州在线广告生成的数据集，并使用此过程的结果生成了一个修改后的原型，其中包括使用机器学习和文本挖掘增强功能。第二个数据集的一个意想不到的结果是在扩展的本体中发现了表情符号的使用。
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.