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. 2022 Nov 16;24(11):e35974. doi: 10.2196/35974

Table 1.

Articles included in the review.

Study Source (duration) Analysis Number of items analyzed
McGregor et al [22], 2014 Web-based forums, Facebook, Twitter, and YouTube (not available)

  • Thematic and content analysis of glaucoma-related posts on the following:

    • Analysis of the nature of the post (personal stories, information sharing or flagging, supportive comments, questions, answers, and general discussions)

    • Sentiment analysis (positive or negative)

 3785 items
Cavazos-Rehg et al [23], 2015 Twitter (February to March 2014)

  • Cannabis-related chatter by influential users on the following:

    • Sentiment analysis by using the Likert scale

    • Thematic analysis of tweets

    • Demographic analysis

 7000 tweets
Daniulaityte et al [24], 2015 Twitter (October to December 2014)

  • US dab-related tweets:

    • Counting and normalizing based on cannabis legalization policy

 125,255 tweets (27,018 geolocated tweets)
Gonzalez-Estrada et al [25], 2015 YouTube (June 4-8, 2014)

  • Content analysis of asthma-related videos on the following:

    • Source: professional society, media, asthma care provider, etc

    • Content: personal experience, medical professional, advertisement, patient education, alternative treatment, or to increase awareness

    • Quality scoring of misleading and useful info

    • Video characteristics or video statistics

 200 most viewed videos
Krauss et al [26], 2015 YouTube (January 22, 2015)

  • Analysis of dabbing-related videos on the following:

    • Characteristics of the people dabbing (age and skills)

    • Characteristics of the session

    • Messages included in the videos

 116 videos
Thompson et al [27], 2015 Twitter (March 2012 to July 2013)

  • Content analysis of cannabis-related tweets and retweets on the following:

    • Adolescence users (age, inferred from the user profile)

    • Sentiment (positive, negative, or unclear)

    • Subject (self, other, general, or subject unclear)

    • Use category (own use, use by others, or not mentioned)

    • Related behaviors (habitual use, social aspect, etc)

    • Positive aspects (better than other drugs and medical use)

 36,939 original tweets and 10,000 retweets
Cavazos-Rehg et al [28], 2016 Twitter (January 2015)

  • Dabbing-related tweets:

    • Thematic analysis of tweets to 7 themes

    • Subanalysis of 1 theme (extreme effects) into physiological or psychological effects

    • Geotagged tweets analysis for number per state

    • Demographic analysis

 5000 tweets
Lamy et al [29], 2016 Twitter (May to July 2015)

  • Content analysis of cannabis edible-related conversations:

    • Tweet sources (media, retail, or users)

    • Sentiment analysis (positive, negative, or neutral)

    • Word frequency analysis

    • Geotagging (policy impact on the volume of tweets)

 3000 tweets
Mitchell et al [30], 2016 Web-based forums (October 2014)

  • Thematic analysis of ADHDa and cannabis web-based forum posts on the following:

    • Impact of cannabis on ADHD symptoms (therapeutic, harmful, both, and none)

    • Other domains (mood, psychiatric conditions, and other [sleep])

    • Comments about cannabis as medicinal (more effective than other ADHD medications, less effective, or not legal)

 268 threads
Andersson et al [31], 2017 Web-based forums (April 18-19, 2016)

  • Thematic analysis of conversations on headache-related posts

 32 topics
Dai and Hao [32], 2017 Twitter (August 2015 to April 2016)

  • Naive Bayes classifier on PTSDb and cannabis-related tweets:

    • Sentiment analysis

    • Analysis of prevalence of support of cannabis use for PTSD in association with state level legislation and socioeconomic factors

 66,000 cannabis-related and 31,184 geolocated tweets
Greiner et al [33], 2017 Web-based forums (November 2014 to March 2015)

  • Content analysis of cannabis help forums on the following:

    • Fields of interest (illness-related, social, financial, and legal issues)

    • Self-help mechanisms (exchange of information, emotional support, group support)

    • Analysis of sex and age when available

    • Highly involved vs moderately involved users

 717 posts
Turner and Kantardzic [34], 2017 Twitter (August 2015 to April 2016)

  • Supervised and unsupervised machine learning techniques of cannabis-related tweets:

    • Binary classification to identify marijuana-related tweet

    • Topic modeling

    • User social network analysis

    • Spatiotemporal analysis of conversations

 40,509 geolocated tweets
Westmaas et al [35], 2017 Web-based forums (January 2000 to December 2013)

  • Topic modeling of Cancer Survivors Network:

    • Analyze smoking or cessation-related content

    • Analysis to determine the overall context in which these discussions occurred

 468,000 posts
Yom Tov and Lev Ran [36], 2017 Bing logs (November 2016 to April 2017)

  • Statistical analysis of cannabis-related query logs

 Not available
Cavazos-Rehg et al [37], 2018 YouTube (June 10-11, 2015)

  • Cannabis review web-based videos:

    • Sentiment analysis

    • Physical or mental effects; is it promotional, encourage follow-up; depiction of consumption; video details and engagement statistics

    • Current users survey (demographics, reason for use, and use of reviews)

 83 videos
Glowacki et al [38], 2018 Twitter (August to October 2016)

  • Statistical analysis on opioid-related tweets:

    • Clustering algorithm to find topics

    • Analysis of trending hashtags, top influencers, and location of tweets

 73,235 tweets
Meacham et al [39], 2018 Reddit (January 2010 to December 2016)

  • Analysis of modes of cannabis use mentions on Twitter on the following:

    • Most frequent words

    • Mentions of adverse effects

    • Subjective highness

 400,000 posts
Leas et al [40], 2019 Google Trends (January 2004 to April 2019)

  • Analysis on CBDc and cannabidiol terms to evaluate public interest

 Not available
Meacham et al [41], 2019 Reddit (January 2017 to December 2019)

  • Content analysis of dabbing-related questions on the following:

    • Topics of questions

    • After engagement and the types and sentiment of information

 193 questions
Nasralah et al [42], 2019 Twitter (January 2015 to February 2019)

  • Analysis of opioid-dependent user’s tweets:

    • Thematic analysis of conversations

    • Demographic analysis

 20,609 tweets
Pérez-Pérez et al [43], 2019 Twitter (February to August 2018)

  • Lexicon- and rule-based analysis of bowel disease tweets on sentiments, network, gender, geolocation, symptoms, and food

 24,634 tweets
Shi et al [44], 2019 Google Trends and Buzzsumo (January 2011 to July 2018)

  • Google Trends analysis on cancer therapies to evaluate interest in cannabis vs other therapies

 Not available
Allem et al [45], 2020 Twitter (May to December 2018)

  • Topic analysis of cannabis-related tweets

 60,861 nonbot and 8874 bot tweets
Janmohamed et al [46], 2020 Blogs, news, forums, and <1% other (August 2019 to April 2021)

  • Topic modeling on vaping-related conversations:

    • Analysis of word prevalence

    • Analysis of change of topics over time

 4,027,172 documents or blogs
Jia et al [47], 2020 Google, Facebook, and YouTube (September 2019)

  • Content analysis of glaucoma and CBD posts on the following:

    • General discussion, information sharing, personal story, question, answer, and moderator comment

    • Quality of information

    • Source of information being professional or not and whether an opinion on glaucoma and medical cannabis use was expressed

    • Analysis of professional accounts

 51 Google websites, 126 Facebook posts, and 37 YouTube videos
Leas et al [48], 2020 Reddit (January 2014 to August 2019)

  • Content analysis of reasons for CBD use:

    • Reasons for personal use (condition and wellness)

    • Analysis based on categorized diagnosable conditions

 104,917 posts
Merten et al [49], 2020 Pinterest (July 31, August 18, and September 1, 2018)

  • Content analysis of CBD and cannabidiol posts on the following:

    • Mentions of mental and physical benefits

    • Emotional appeal analysis

    • Engagement statistics

 1280 pins
Mullins et al [50], 2020 Twitter (June to July 2017)

  • Analysis of Ireland pain-related tweets on:

    • Topic analysis: sentiment analysis, analysis of most frequently occurring keywords, demographic analysis, and personal use analysis

 941 tweets
Saposnik and Huber [51], 2020 Google Trends (January 2004 to December 2019)

  • Google Trends analysis on autism and cannabis to analyze trends in search volume about the causes and treatments of Autism spectrum disorder over time

 Not available
Song et al [52], 2020 GoFundMe (January 2012 to December 2019)

  • Content analysis of alternative medicine and cancer campaigns on the following:

    • Themes of patient narratives

    • Types of alternative treatments used

    • Demographics (gender, cancer type, cancer stage, insurance status, past treatment, future treatment, and alternative treatment)

 1474 campaigns
Tran and Kavuluru [53], 2020 Reddit and or FDA comments (January to April 2019)

  • Content analysis on CBD posts for therapeutic effects and popular modes of consumption compared with FDAd comments

 64,099 Reddit and 3832 FDA comments
van Draanen et al [54], 2020 Twitter (January 2017 to June 2019)

  • Cannabis-related US and Canada posts:

    • Topic modeling

    • Sentiment analysis based on cannabis legalization policies

 1,200,127 tweets
Zenone et al [55], 2020 GoFundMe (January 2017 to March 2019)

  • Thematic analysis of cancer and cannabis campaigns:

    • Efficacy claims

    • Treatment regimen classification

    • CBD efficacy presentation

    • Content analysis for Other: cancer stage, raised money, and number of donors

 155 campaigns
Pang et al [56], 2021 Twitter (December 2019 to December 2020)

  • Thematic analysis of pregnancy- and cannabis-related tweets for safety during pregnancy, safety postpartum, and pregnancy-related symptoms

 17,238 tweets
Rhidenour et al [57], 2021 Reddit (January 2008 to December 2018)

  • Thematic analysis of veteran’s cannabis posts on the following:

    • Point of view, reasons for use, prescription drug use, or other substance use

    • Test, legality, legal policy, and doctor-patient conversation

 974 posts
Smolev et al [58], 2021 Facebook (November 2018 to November 2019)

  • Thematic analysis of traumatic brachial plexus injury posts on: antiopioid sentiment, preference for alternative options, and antigabapentin sentiment

 7694 posts
Soleymanpour et al [59], 2021 Twitter (July 2019)

  • Analysis of CBD marketing tweets and therapeutic claims

 2,200,000 tweets
Zenone et al [60], 2021 GoFundMe (June 2017 to May 2019)

  • Thematic analysis for informational pathways: self-directed research, recommendations from a trusted care provider, and insights shared by someone associated with or influencing the crowd funders personal network

  • Content analysis for intended outcome, social media shares, number of donors, total requested, and total received

 164 campaigns
Turner et al [62] 2021 Twitter (October 2019 to January 2020)

  • Analysis of personal and commercial CBD-related tweets; term and sentiment analysis

 167,755 personal 143,322 commercial tweets
Allem et al [61], 2022 Twitter (January to September 2020)

  • Analysis of cannabis-related conversation for health-related motivations or perceived adverse health effects

 353,353 tweets
Meacham et al [63] 2022 Reddit (December 2015 to August 2019)

  • Analysis of cannabis-related posts from an opioid use and an opioid recovery subreddit

 908 posts from opioid recovery subreddits and 4224 posts from opioid use subreddits

aADHD: attention-deficit hyperactivity disorder.

bPTSD: posttraumatic stress disorder.

cCBD: cannabidiol.

dFDA: Food and Drug Administration.