JMIR Publications

ABSTRACT

Background:

Living kidney donation (LKD) currently constitutes approximately a quarter of all kidney transplant donors. There exist barriers that preclude prospective donors from donating such as medical ineligibility and cost associated with donation. A better understanding of the perceptions as well as barriers to living donation can facilitate the development of effective policies, education opportunities, and outreach strategies, which may lead to increased number of LKD. Prior research focused predominantly on the perceptions and barriers experienced by a small subset of individuals who have prior exposure to the donation process. The viewpoints of the general public are rarely represented in prior research.

Objective:

The current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. A resultant dataset was made available to public domain to facilitate further investigation on this topic.

Methods:

We collected comments using web-scraping tools in Python from the New York Times (NYT), as well as YouTube, Twitter, and the forum site Reddit. We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning.

Results:

203,219 unique comments were collected from the above sources. The deep neural network model resulted in 84% accuracy on testing data. Further validation of predictions found an actual accuracy of 63%. The final database contains 11,027 comments classified as being related to LKD.

Conclusions:

The current study laid out the groundwork for more comprehensive analysis of the perceptions, myths and feelings about LKD. The web-scraping and machine learningclassifier are effective methods to collect and examine opinions on LKD held by the general public.