Human Trafficking of children is a serious crime that continues to plague the United States. With the rise of computing technologies, the internet has become one of the main mediums through which this crime is facilitated. Fortunately, these online activities leave traces which are invaluable to law enforcement agencies trying to stop human trafficking. However, identifying and intervening with these cases is still a challenging task. The sheer volume of online activity makes it difficult for law enforcement to efficiently identify any potential leads. To compound this issue, traffickers are constantly changing their techniques online to evade detection. Thus, there is a need for tools to efficiently sift through all this online data and narrow down the number of potential leads that a law enforcement agency can deal with. While some tools and prior research do exist for this purpose, few of these tools target review sites (sometimes referred to as john boards), and most tools focus solely on the victims, ignoring the traffickers and customers who facilitate the trafficking. Additionally, none of these tools adequately address law enforcement user needs for information visualizations. The purpose of this project is therefore to build a prototype for a tool that assists law enforcement with identifying potential victims and those who exploit them. In particular, this work will create models to assist with identifying victims from online sites using machine learning and use social network analysis to uncover the customers who exploit those children. The researcher will then create a functional prototype that incorporates these models into a visual analytics system. This work is informed by an initial interview study with law enforcement officials and an initial research prototype.
Note: This project contains a research and/or development component, as defined in applicable law," and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). ca/ncf