Linking Multisite Sex Ad Data at the Individual Level to Aid Counter-Trafficking Efforts
研究提出一个端到端流程，利用网络科学、信息系统和人工智能技术，在个体层面链接多个成人服务网站的性广告数据，并过滤错误链接，已帮助识别60多名潜在性贩卖受害者。
Problem definition: The internet facilitates sex trafficking through adult service websites (ASWs) that host online advertisements for sexual services (sex ads). Since the closure of the popular site Backpage.com, the ecosystem of ASWs has expanded to include multiple competing sites that are hosted outside U.S. jurisdiction. Gaining intelligence for counter-trafficking efforts requires collecting, linking, and cleaning the data from multiple sites. However, high ad volumes, disparate data types, and the existence of generic and misappropriated data make this process challenging. We present an end-to-end process for linking sex ad data and filtering potentially erroneous links. Outputs of the developed process have been used to inform counter-trafficking operations that have helped identify more than 60 potential victims of sex trafficking, some of whom are getting help to transition out of the life. Methodology/results: Our process leverages concepts and techniques from network science, information systems, and artificial intelligence to link ads across sites at the level of an individual or unique posting entity. Our approach is computationally efficient, allowing millions of ads to be processed in under an hour. A key component of our process is an edge-filtering procedure that identifies and removes potentially erroneous links in a graph representation of sex ad data. A comparison of the proposed process to an existing approach shows that our process is typically more computationally efficient and yields substantial increases in the number of individuals for which we can derive actionable intelligence. Managerial implications: The proposed process is an efficient and effective approach for transforming the high volumes of disparate data from sex ads into intelligence that can save lives. It has been refined over years of collaboration with practitioners and represents a strong foundation upon which further counter-trafficking tools can be built. Funding: This research is partially supported by the National Science Foundation [Grant D-ISN-2240299].