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Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study
BACKGROUND: Living 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 li...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682456/ https://www.ncbi.nlm.nih.gov/pubmed/36346661 http://dx.doi.org/10.2196/37884 |
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author | Asghari, Mohsen Nielsen, Joshua Gentili, Monica Koizumi, Naoru Elmaghraby, Adel |
author_facet | Asghari, Mohsen Nielsen, Joshua Gentili, Monica Koizumi, Naoru Elmaghraby, Adel |
author_sort | Asghari, Mohsen |
collection | PubMed |
description | BACKGROUND: Living 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: The 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. |
format | Online Article Text |
id | pubmed-9682456 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-96824562022-11-24 Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study Asghari, Mohsen Nielsen, Joshua Gentili, Monica Koizumi, Naoru Elmaghraby, Adel JMIR Med Inform Original Paper BACKGROUND: Living 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: The 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. JMIR Publications 2022-11-08 /pmc/articles/PMC9682456/ /pubmed/36346661 http://dx.doi.org/10.2196/37884 Text en ©Mohsen Asghari, Joshua Nielsen, Monica Gentili, Naoru Koizumi, Adel Elmaghraby. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.11.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Asghari, Mohsen Nielsen, Joshua Gentili, Monica Koizumi, Naoru Elmaghraby, Adel Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title | Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title_full | Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title_fullStr | Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title_full_unstemmed | Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title_short | Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study |
title_sort | classifying comments on social media related to living kidney donation: machine learning training and validation study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682456/ https://www.ncbi.nlm.nih.gov/pubmed/36346661 http://dx.doi.org/10.2196/37884 |
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