How Is Marijuana Vaping Portrayed on YouTube? Content, Features, Popularity and Retransmission of Vaping Marijuana YouTube Videos

Yang, Qinghua; Sangalang, Angeline; Rooney, Molly; Maloney, Erin K.; Emery, Sherry; Cappella, Joseph N.

doi:10.1080/10810730.2018.1448488

Cited by 26 publications

(32 citation statements)

References 46 publications

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“…The operationlization of theme was adapted by a previous content analysis conducted by Sinnenberg and colleagues (2016), who categorized the semantic content of tweets into risk factor , awareness , treatment and management , mechanism , outcomes , symptoms , prevention , and support (see Table 2). The source was coded as individuals (e.g., John Moss; Michael), health-related organizations (e.g., U-M Health System), and non-health-related organizations (e.g., WiFi in Japan), which was adapted from previous content analytic health communication studies on social media (e.g., Stellefson et al, 2014; Yang et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

Yang

Tufts

Ungar

et al. 2018

Journal of Health Communication

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Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets’ novelty, utility, theme and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and non-health organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

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Section: Methodsmentioning

confidence: 99%

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

Yang

Tufts

Ungar

et al. 2018

Journal of Health Communication

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“…For studies on noncommunicable diseases (9/69) or treatments (5/69), YouTube was the most used social media platform. Overall, health misinformation was most prevalent in studies related to smoking products, such as hookah and water pipes [33,59,71], e-cigarettes, and drugs, such as opioids and marijuana [45,70,97]. Health misinformation about vaccines was also very common.…”

Section: Prevalence Of Health Misinformationmentioning

confidence: 99%

“…According to topic, regarding drug and opioid use, studies investigated the dissemination of misinformation through social media platforms [32,45,46,70,97], the consumption of misinformation related to these products, drug abuse, and the sale of online medical products [61,66]. These studies highlighted the risk, especially for young people, caused by the high rate of misinformation related to the dissemination of drug practice and misuse (predominantly marijuana and opioids) [45].…”

Section: Drugs and Smokingmentioning

confidence: 99%

Prevalence of Health Misinformation on Social Media: Systematic Review

2021

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Background Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. Objective This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. Methods We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. Results A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%). Conclusions The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

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“…Only two studies have examined how cannabis vaping is portrayed on YouTube and were based on videos searched up to 2015. One study by, Krauss and colleagues, examined dabbing [22] and another by Yang et al reviewed video content for cannabis vaping based on videos published between July 2014 and June 2015 [23]. A more recent study identified a number of videos that portrayed modifications to electronic nicotine delivery systems that can be used to deliver substances other than nicotine.…”

Section: Introductionmentioning

confidence: 99%

Content analysis of cannabis vaping videos on YouTube

et al. 2021

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Background and AimsThere has been an increase in the potency of cannabis during the last two decades and adoption of a novel method of administration-vaping. YouTube, a social media platform, has become a popular source to access cannabis-related information. This study aimed to identify cannabis vaping YouTube videos from 2016 to 2020 and examine the themes and metrics. Design Cross-sectional sample of 200 YouTube videos. Setting YouTube, an on-line video sharing platform. Measurements Videos related to cannabis vaping were identified using the search terms:'vaping cannabis', 'vaping weed', 'vaping marijuana' and 'vaping THC' [tetrahydrocannabinol]. Videos were independently coded by two researchers. The number of views, likes, dislikes and comments were also collected. Robust regression was used to analyse the relationship between identified video themes and video metrics. Findings Six themes were identified: 'advertisement', 'product review', 'celebratory ', 'reflective', 'how-to' and 'warning'. The 'how-to' and 'celebratory' videos received the highest number of views and likes. The most popular video was viewed more than 4 000 000 times. Many videos portrayed risky behaviour (e.g. vaping a whole THC cartridge in a single setting). Fifty-two percent of these videos had no age access restrictions. The robust regression model also found that engagement metric was positively associated with 'reflective' videos and negatively associated with 'advertisement' videos. Conclusions A large number of videos on cannabis vaping are available on-line without age-restriction. Videos that portrayed risky behaviour appear to be prevalent.

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How Is Marijuana Vaping Portrayed on YouTube? Content, Features, Popularity and Retransmission of Vaping Marijuana YouTube Videos

Cited by 26 publications

References 46 publications

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

Prevalence of Health Misinformation on Social Media: Systematic Review

Content analysis of cannabis vaping videos on YouTube

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