TY  - JOUR
AU  - Asghari, Mohsen
AU  - Nielsen, Joshua
AU  - Gentili, Monica
AU  - Koizumi, Naoru
AU  - Elmaghraby, Adel
PY  - 2022
DA  - 2022/11/8
TI  - Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study
JO  - JMIR Med Inform
SP  - e37884
VL  - 10
IS  - 11
KW  - living kidney donation
KW  - kidney donation
KW  - kidney transplantation
KW  - text mining
KW  - web scraping
KW  - NLP
KW  - deep learning
KW  - neural network
KW  - barriers to kidney donation
KW  - barriers
KW  - awareness
KW  - perception
KW  - machine learning
KW  - online source
KW  - online comments
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2022/11/e37884
UR  - https://doi.org/10.2196/37884
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36346661
DO  - 10.2196/37884
ID  - info:doi/10.2196/37884
ER  -