JMIR Publications

ABSTRACT

Background:

Medicinal cannabis is increasingly being used for a variety of physical and mental health conditions. Social media and online health platforms provide a valuable real time and cost-effective surveillance resource for individuals who use cannabis for medicinal purposes. This is especially important considering evidence for the optimal use of medicinal cannabis is still emerging. Despite the online marketing of medicinal cannabis to consumers, currently, there is no robust, regulatory framework to measure clinical health benefit or individual experience of adverse events.

Objective:

We reviewed research approaches and methodologies of studies that utilize online user-generated text to study the use of cannabis as a medicine.

Methods:

We conducted the review using PRISMA guidelines, searching Medline, Scopus, Web of Science and Embase databases from their respective inceptions until May 2021. Studies were included if they aimed to understand online user-generated text related to health conditions where cannabis is used as a medicine, or where health was mentioned in general cannabis conversations.

Results:

Thirty-eight articles were included in the review. Of these, Twitter was used three times more than other computer-generated sources including Reddit, online forums, GoFundMe, YouTube, and Google Trends. Analytic methods included sentiment assessment, thematic analysis (manual and automatic), social network analysis, and geographic analysis.

Conclusions:

This study is the first to systematically review techniques utilized by research on consumer-generated text for understanding cannabis as a medicine. It is increasingly evident that consumer-generated data offers opportunities for a greater understanding of individual behavior, population health outcomes. Yet research using this data has some limitations that include difficulties in establishing sample representativeness, and a lack of methodological best practice. To address these, publicly available de-identified annotated data sources; determination of posts origins (organizations, bots, power users, or ordinary individuals); and more powerful analytical techniques can be employed.