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Title: Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study
BackgroundLiving kidney donation currently constitutes approximately a quarter of all kidney donations. There exist barriers that preclude prospective donors from donating, such as medical ineligibility and costs associated with donation. A better understanding of perceptions of and barriers to living donation could facilitate the development of effective policies, education opportunities, and outreach strategies and may lead to an increased number of living kidney donations. Prior research focused predominantly on perceptions and barriers among a small subset of individuals who had prior exposure to the donation process. The viewpoints of the general public have rarely been represented in prior research. ObjectiveThe current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. The resultant data set was made available in the public domain to facilitate further investigation of this topic. MethodsWe collected comments using Python-based web-scraping tools from the New York Times, YouTube, Twitter, and 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. ResultsA total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation. ConclusionsThe current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation.  more » « less
Award ID(s):
1838306
PAR ID:
10529271
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
JMIR
Date Published:
Journal Name:
JMIR Medical Informatics
Volume:
10
Issue:
11
ISSN:
2291-9694
Page Range / eLocation ID:
e37884
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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