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Accepted for/Published in: JMIR Formative Research

Date Submitted: Nov 17, 2021
Date Accepted: Jan 21, 2022

The final, peer-reviewed published version of this preprint can be found here:

Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets

Allem JP, Majmundar A, Dormanesh A, Donaldson S

Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets

JMIR Form Res 2022;6(2):e35027

DOI: 10.2196/35027

PMID: 35212637

PMCID: 8917433

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Identifying health-related discussions of cannabis use on Twitter: a proof-of-concept study

  • Jon-Patrick Allem; 
  • Anuja Majmundar; 
  • Allison Dormanesh; 
  • Scott Donaldson

ABSTRACT

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 of 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 (UMLS) Consumer Health Vocabulary (CHV), could identify cannabis-related motivations of use and health consequences of its use as discussed on Twitter 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 one of 17 a priori categories of commonly health-related topics, using a rule-based classifier with each category defined by the terms in the medical dictionary. A subsample of posts (n=1094) was then manually annotated to help validate the rule-based classifier and determine if each post pertained to health-related motivations for cannabis use or perceived adverse health effects from its use or neither.

Results:

The validation process suggested that the medical dictionary could identify health-related conversations in 31.2% of posts. Specifically, 20.4% of posts were accurately identified as relating to a health-related motivation for cannabis use, while 10.8% of posts were accurately identified as relating to a health-related consequence from cannabis use. Potential health-related conversations around cannabis use ranged from issues with the respiratory system and stress to the immune system and gastrointestinal problems, among other health topics.

Conclusions:

The mining of social media data may prove helpful in improving surveillance of cannabis products and their adverse health effects. However, future research needs to develop and validate a dictionary and codebook that captures cannabis use-specific health conversations on Twitter.


 Citation

Please cite as:

Allem JP, Majmundar A, Dormanesh A, Donaldson S

Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets

JMIR Form Res 2022;6(2):e35027

DOI: 10.2196/35027

PMID: 35212637

PMCID: 8917433

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