Identifying Kidney Trade Networks using Web Scraping Data

Abstract

Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts of the world including South Asia, Central America, the Middle East, and East Asia. Kidney trade networks reported in these hotspots are often complex systems involving several players including buyers, sellers, and brokers operating across international borders. In particular, brokers are known to arrange buyers and sellers from different countries, mobilizing them to another country for surgery so that they can bypass domestic laws in sellers’ and buyers’ countries. The exact patterns of the country networks are, however, largely unknown due to the lack of a systematic approach to collect the data. Most of the kidney trade network information is currently available in the form of news articles, case studies, and reports, and no comprehensive database exists at this time. To this end, the present study explored online newspaper scraping to systematically compile the information of transnational kidney trade networks. Specific pieces of information about surgery locations as well as the nationalities of buyers and sellers were recorded to visualize the country networks of transnational kidney trade. The findings of the study suggest that newspaper scraping is a promising approach to compile such data especially in the dire shortage of empirical data.

Note: Funding: This work is funded by NSF - EAGER: ISN: / 1838306.

Declaration of Interests: None to declare.

Keywords: Kidney trafficking, Machine learning, Web-scraping data, Trafficking hub, South Asia, Newspaper articles