Analyzing network of organ sales and trafficking using web scraping data

Abstract

Background: Many countries have been enforcing domestic law against kidney sales to make sure that vulnerable people are not misused. Concurrently, the transnational transplant tourism has been on the rise and organ trafficking cases have been identified worldwide. Thus, several regional hubs are emerging in most part of the world including South Asia, Central America, Middle East, and East Asia. The South Asian hub comprised of India, Pakistan, Nepal, and Bangladesh is one of the most discussed ones. This is a complex network of organ trade that includes a complex relationship among the buyers, sellers, and brokers at the international borders. These brokers mostly arrange buyers and sellers from different countries and mobilize them to another country for surgery so that they can bypass the domestic laws. However, there have been no scientific studies thus far to identify these networks and patterns of transactions. Moreover, this field lacks scientific tools and empirical data that can help understand the extent of the organ trade problems. This paper aims to develop a web base algorithm to gather data from newspaper articles from the regional hubs of organ trade and analyze the data.

Methods: We use a web scraping method to collect articles from regional newspaper websites based on very carefully selected search terms: kidney + “trafficking,” “trade,” “buying,” “selling,” “market,” “bazaar,” “sales,” and “illegal transplant.” To execute this search actions, we use Python libraries: Selenium, NLTK, GeoText, Requests, and BeautifulSoup. We extracted article fragments from the gathered universe which contain locations (cities, states, countries) and variations on buying, selling, surgery (synonyms/word forms) to identify networks between the three mentioned in the articles. After removing the duplicates, we check identified networks by hand.

Results: We have collected more than 4 thousand unique new paper articles and after analyzing them we have found approximately 200 unique mentions of networks of buyers, sellers, and surgery countries. The results help to clearly separate the country which has most nodes of either buyers, sellers, or surgeries or a combination of them. We find that while most surgeries are taken place in India, the sellers are mainly from Nepal and Bangladesh. However, the buyers are mostly from South Asian economies without a significant pattern, there are also buyers from the Middle East and Gulf countries. The surgeries have also taken place in Sri Lanka and Thailand.

Conclusions: The results support our initial hypothesis that there are strong networks of organ trade comprised of transnational actors. This machine learning approach to complement the scarce data on organ trade appears to be a promising tool and can be expanded to analyze networks of similar illicit trades around the world.