Utilizing Text Analytics to Assess Intern'l Human Trafficking Patterns
May 28th 2018 09:20 - 09:40
The US Department of State (DOS) and other humanitarian agencies have a vested interest in assessing and preventing human trafficking in its many forms. A subdivision within the DOS releases publicly facing Trafficking in Persons (TIP) reports for more than 200 countries annually. These reports are entirely freeform text, though there is a richness of structure hidden within the text. How can decision makers quickly tap this information for patterns in international human trafficking?
This presentation showcases a strategy of applying SAS® Text Analytics to explore the TIP reports and apply new layers of structured information. Specifically, we will identify common themes across the reports, use topic analysis to identify a structural similarity between reports in identifying source and destination countries for trafficking, and utilize a rule-building approach to extract these relationships from freeform text. We will subsequently depict these trafficking relationships across multiple countries in the form of a geographic network diagram, which covers the types of trafficking as well as whether the countries involved are invested in addressing the problem. This ultimately provides decision makers with big picture information on how to best combat human trafficking internationally.