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Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets
BACKGROUND: The cannabis product and regulatory landscape is changing in the United States. Against the backdrop of these changes, there have been increasing reports on health-related motives for cannabis use and adverse events from its use. The use of social media data in monitoring cannabis-relate...
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/PMC8917433/ https://www.ncbi.nlm.nih.gov/pubmed/35212637 http://dx.doi.org/10.2196/35027 |
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author | Allem, Jon-Patrick Majmundar, Anuja Dormanesh, Allison Donaldson, Scott I |
author_facet | Allem, Jon-Patrick Majmundar, Anuja Dormanesh, Allison Donaldson, Scott I |
author_sort | Allem, Jon-Patrick |
collection | PubMed |
description | BACKGROUND: The cannabis product and regulatory landscape is changing in the United States. Against the backdrop of these changes, there have been increasing reports on health-related motives for cannabis use and adverse events from its use. The use of social media data in monitoring cannabis-related health conversations may be useful to state- and federal-level regulatory agencies as they grapple with identifying cannabis safety signals in a comprehensive and scalable fashion. OBJECTIVE: This study attempted to determine the extent to which a medical dictionary—the Unified Medical Language System Consumer Health Vocabulary—could identify cannabis-related motivations for use and health consequences of cannabis use based on Twitter posts in 2020. METHODS: Twitter posts containing cannabis-related terms were obtained from January 1 to August 31, 2020. Each post from the sample (N=353,353) was classified into at least 1 of 17 a priori categories of common health-related topics by using a rule-based classifier. Each category was defined by the terms in the medical dictionary. A subsample of posts (n=1092) was then manually annotated to help validate the rule-based classifier and determine if each post pertained to health-related motivations for cannabis use, perceived adverse health effects from its use, or neither. RESULTS: The validation process indicated that the medical dictionary could identify health-related conversations in 31.2% (341/1092) of posts. Specifically, 20.4% (223/1092) of posts were accurately identified as posts related to a health-related motivation for cannabis use, while 10.8% (118/1092) of posts were accurately identified as posts related to a health-related consequence from cannabis use. The health-related conversations about cannabis use included those about issues with the respiratory system, stress to the immune system, and gastrointestinal issues, among others. CONCLUSIONS: The mining of social media data may prove helpful in improving the surveillance of cannabis products and their adverse health effects. However, future research needs to develop and validate a dictionary and codebook that capture cannabis use–specific health conversations on Twitter. |
format | Online Article Text |
id | pubmed-8917433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-89174332022-03-13 Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets Allem, Jon-Patrick Majmundar, Anuja Dormanesh, Allison Donaldson, Scott I JMIR Form Res Original Paper BACKGROUND: The cannabis product and regulatory landscape is changing in the United States. Against the backdrop of these changes, there have been increasing reports on health-related motives for cannabis use and adverse events from its use. The use of social media data in monitoring cannabis-related health conversations may be useful to state- and federal-level regulatory agencies as they grapple with identifying cannabis safety signals in a comprehensive and scalable fashion. OBJECTIVE: This study attempted to determine the extent to which a medical dictionary—the Unified Medical Language System Consumer Health Vocabulary—could identify cannabis-related motivations for use and health consequences of cannabis use based on Twitter posts in 2020. METHODS: Twitter posts containing cannabis-related terms were obtained from January 1 to August 31, 2020. Each post from the sample (N=353,353) was classified into at least 1 of 17 a priori categories of common health-related topics by using a rule-based classifier. Each category was defined by the terms in the medical dictionary. A subsample of posts (n=1092) was then manually annotated to help validate the rule-based classifier and determine if each post pertained to health-related motivations for cannabis use, perceived adverse health effects from its use, or neither. RESULTS: The validation process indicated that the medical dictionary could identify health-related conversations in 31.2% (341/1092) of posts. Specifically, 20.4% (223/1092) of posts were accurately identified as posts related to a health-related motivation for cannabis use, while 10.8% (118/1092) of posts were accurately identified as posts related to a health-related consequence from cannabis use. The health-related conversations about cannabis use included those about issues with the respiratory system, stress to the immune system, and gastrointestinal issues, among others. CONCLUSIONS: The mining of social media data may prove helpful in improving the surveillance of cannabis products and their adverse health effects. However, future research needs to develop and validate a dictionary and codebook that capture cannabis use–specific health conversations on Twitter. JMIR Publications 2022-02-25 /pmc/articles/PMC8917433/ /pubmed/35212637 http://dx.doi.org/10.2196/35027 Text en ©Jon-Patrick Allem, Anuja Majmundar, Allison Dormanesh, Scott I Donaldson. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.02.2022. 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 Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Allem, Jon-Patrick Majmundar, Anuja Dormanesh, Allison Donaldson, Scott I Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title | Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title_full | Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title_fullStr | Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title_full_unstemmed | Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title_short | Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets |
title_sort | identifying health-related discussions of cannabis use on twitter by using a medical dictionary: content analysis of tweets |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917433/ https://www.ncbi.nlm.nih.gov/pubmed/35212637 http://dx.doi.org/10.2196/35027 |
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