%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 6
%N 2
%P e35027
%T Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets
%A Allem,Jon-Patrick
%A Majmundar,Anuja
%A Dormanesh,Allison
%A Donaldson,Scott I
%+ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto Street, SSB 312D, Los Angeles, CA, 90032, United States, 1 323 442 7921, allem@usc.edu
%K cannabis
%K marijuana
%K Twitter
%K social media
%K adverse event
%K cannabis safety
%K dictionary
%K rule-based classifier
%K medical
%K health-related
%K conversation
%K codebook
%D 2022
%7 25.2.2022
%9 Original Paper
%J JMIR Form Res
%G English
%X 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. 
%M 35212637
%R 10.2196/35027
%U https://formative.jmir.org/2022/2/e35027
%U https://doi.org/10.2196/35027
%U http://www.ncbi.nlm.nih.gov/pubmed/35212637