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. 2022 Sep;7(9):e009803.
doi: 10.1136/bmjgh-2022-009803.

Identifying kidney trade networks using web scraping data

Meng-Hao Li  1 , Abu Bakkar Siddique  1 , Brian Wilson  1 , Amit Patel  2 , Hadi El-Amine  3 , Naoru Koizumi  4
Affiliations

Identifying kidney trade networks using web scraping data

Meng-Hao Li et al. BMJ Glob Health. 2022 Sep.

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 hot spots are often complex systems involving several players such as buyers, sellers and surgery countries operating across international borders 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 information is currently available in the form of case studies, court materials and news articles or reports, and no comprehensive database exists at this time. The present study thus explored online newspaper scraping to systematically collect 10 419 news articles from 24 major English newspapers in South Asia (January 2016 to May 2019) and build transnational kidney trade networks at the country level. Additionally, this study applied text mining techniques to extract words from each news article and developed machine learning algorithms to identify kidney trade and non-kidney trade news articles. Our findings suggest that online newspaper scraping coupled with the machine learning method is a promising approach to compile such data, especially in the dire shortage of empirical data.

Keywords: Control strategies; Health policy.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Flow chart of article collection to network extraction.
Figure 2
Figure 2
Word selection for machine learning (ML) algorithm development.
Figure 3
Figure 3
Text classification process.
Figure 4
Figure 4
Kidney trade networks using data from Region I news articles.
Figure 5
Figure 5
Kidney trade networks using data from Region II news articles.
See this image and copyright information in PMC

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