Objectives. To use publicly accessible data from people who post to Twitter to rapidly capture and describe the public's recent experiences with cannabis.Methods. We obtained Twitter posts containing cannabis-related terms from May 1, 2018, to December 31, 2018. We used methods to distinguish between posts from social bots and nonbots. We used text classifiers to identify topics in posts (n = 60 861).Results. Prevalent topics of posts included using cannabis with mentions of cannabis initiation, processed cannabis products, and health and medical with posts suggesting that cannabis could help with cancer, sleep, pain, anxiety, depression, trauma, and posttraumatic stress disorder. Polysubstance use was a common topic with mentions of cocaine, heroin, ecstasy, LSD, meth, mushrooms, and Xanax along with cannabis. Social bots regularly made health claims about cannabis.Conclusions. Findings suggest that processed cannabis products, unsubstantiated health claims about cannabis products, and the co-use of cannabis with legal and illicit substances warrant considerations by public health researchers in the future.

This research study examines the prevalence of cartoon images promoting e-cigarette–associated products on Instagram and their association with likes from users.

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.

Background

Twitter offers a platform for rapid diffusion of information and its users' attitudes and behaviors. Insights about information propagation via retweets (the message forwarding function) offer observable explanations of ways in which modern human interactions get organized in the form of online networks, and contextualized in the form of public health, policy decisions, disaster management, and civic participation. This study conceptualized and validated the Why We Retweet Scale to contextualize retweeting behavior.

Objective

Twitter users were identified using clustering algorithms that consider a users' position in their network and invited for an online survey. Participants (N = 1433) responded to 19 questions about why they retweet. Exploratory factor Analysis (EFA) was conducted on a scale development sample (70% of original sample), which informed the Confirmatory Factor Analysis (CFA) on a scale testing sample (30% of the original sample). Varimax rotation was used to obtain a rotated factor solution, which resulted in interpretable factors. Demographic differences among scale factors were analyzed using one-way ANOVA or independent samples t-tests.

Results

The final model (χ221 = 28, RMSEA = .03 [90% CI, 0.00-0.06], CFA = .99, TLI = 0.99) represented a parsimonious solution with 4 factors, measured by 2-3 items each, creating a final scale consisting of 9 items. Factor labels and definitions were: (1) Show approval, "Show support to the tweeter"; (2) Argue, "To argue against a tweet that I disagree with"; (3) Gain attention, "Add followers or gain attention"; and (4) Entertain, "Create humor/amusement". Demographic differences were also reported.

Conclusions

The Why We Retweet Scale offers a useful conceptualization and assessment of motivations for retweeting. In the future, communication strategists might consider the factors associated with information propagation when designing campaign messages to maximize message reach and engagement on Twitter.

Importance

Exposure to tobacco-related content on social media may foster positive attitudes toward tobacco products and brands, and influence the likelihood of initiating or continuing use of tobacco, especially among adolescents and young adults.

Objective

To perform the first systematic review and meta-analysis, to our knowledge, on studies that examined the association between exposure to tobacco content on social media and lifetime tobacco use, past 30-day tobacco use, and susceptibility to use tobacco among never users.

Data sources

Tobacco, social media, and marketing search terms were entered into online databases, including MEDLINE, ISI Web of Science, Scopus, and PsychINFO. Study characteristics, including research design and methods, sampling strategy, and demographics, were assessed for each study.

Study selection

Studies reporting odds ratios (ORs) for self-reported exposure to, or experimentally manipulated, tobacco content on social media and lifetime tobacco use, past 30-day tobacco, and susceptibility to use tobacco among never users. The systematic search produced 897 independent articles, of which 29 studies met inclusion criteria.

Data extraction and synthesis

A 3-level random-effects meta-analysis was used to estimate ORs, 95% CIs, and heterogeneity (I2) for each tobacco use outcome. Study quality and publication bias were assessed.

Main outcomes and measures

Lifetime tobacco use, past 30-day tobacco use, and susceptibility to use tobacco among never users. Tobacco use included e-cigarettes, cigarettes, and other (cigar, hookah, smokeless tobacco).

Results

The total sample size across the 24 included datasets was 139 624, including 100 666 adolescents (72%), 20 710 young adults (15%), and 18 248 adults (13%). Participants who were exposed to tobacco content on social media, compared with those who were not exposed, had greater odds of reporting lifetime tobacco use (OR, 2.18; 95% CI, 1.54-3.08; I2 = 94%), past 30-day tobacco use (OR, 2.19; 95% CI, 1.79-2.67; I2 = 84%), and susceptibility to use tobacco among never users (OR, 2.08; 95% CI, 1.65-2.63; I2 = 73%). Subgroup analyses showed similar associations for tobacco promotions, active engagement, passive engagement, lifetime exposure to tobacco content, exposure to tobacco content on more than 2 platforms, and exposure to tobacco content among adolescents and young adults.

Conclusions and relevance

Findings suggest that a comprehensive strategy to reduce the amount of tobacco content on social media should be developed by federal regulators. Such actions may have downstream effects on adolescent and young adult exposure to protobacco content, and ultimately tobacco use behaviors.

There has been a surge in the number of vape shops in the USA. Research on the marketing practices of e-cigarette manufacturers is scarce and even less known are the practices of vape shop retailers. Past research on tobacco marketing has shown differences in the amount and content of marketing material, based on a community's demographic profile. This study examined marketing strategies in vape shops and explored differences among vape shops located in communities that differ by ethnic composition. Data was gathered in 2014 from a pilot-study on vape shops (n=77) in Los Angeles, which documented the characteristics of shops through employee interviews and in-store observations. Data were collected from shops located in communities that were predominantly, African-American (n=20), Hispanic (n=17), Korean (n=18), or non-Hispanic White (n=22). Sixty-one percent of vape shops had advertisements (print ads and posters) for e-cigarettes and 84% offered discounts. Vape shops in Hispanic communities were more likely to have ethnic specific marketing material compared to shops in other communities. All the shops provided customers with free samples, however those in Korean and non-Hispanic White communities had a significantly higher prevalence of customer accessible free samples. Vape shop marketing practices differed by ethnic community. A large majority of shops provided free samples to their customers, a practice which is now banned by the FDA. It will be important to monitor how vape shops will adjust their marketing strategy because of this ban. Future research should expand on the findings presented here to provide regulators with further crucial information.

Background

This study analysed posts to Instagram related to KandyPens, an open-system pod mod e-cigarette company, marketing its products as aromatherapy devices. The objective was to determine themes, corresponding user profiles and references to types of e-liquid solutions used with KandyPens.

Methods

Data consisted of publicly available posts to Instagram with the hashtag '#kandypens' collected from 8 June to 8 August 2018 (n=1775). Identified themes included: product appearance (eg, highlighting design features including colour of device), user experience (eg, vape tricks), flavours (eg, strawberry) and promotions (eg, $10 off). The type of e-liquid solutions (nicotine, aromatherapy and cannabis) used with KandyPens were also recorded. Instagram profiles were categorised into: vaping enthusiast/advocates, influencers, KandyPens' official Instagram account, vape vendors and average Instagram users.

Results

User experience (28.90%) and product appearance (21.80%) were predominant themes followed by promotions (10.08%), and flavours (1.01%). About 32.43 % of posts referenced cannabis-related solutions, 2.98 % of the posts mentioned nicotine-related solutions and 0.11 % of the posts mentioned aromatherapy. Average Instagram users (24.89%) posted the majority of posts followed by vape vendors (20.72%), KandyPens' official account (17.96%), vaping enthusiasts/advocates (10.75%) and influencers (0.45%).

Conclusion

KandyPens markets its products as aromatherapy devices; however, Instagram posts related to these products rarely mentioned their purported purpose. Future research should consider product design, user experience and the co-use of nicotine and cannabis with KandyPens to assess implications related to product appeal and abuse liability.

Social media platforms offer opportunities for targeted health communication ads to improve tobacco and cannabis prevention efforts. This study described tobacco and cannabis-related ads targeted towards adolescents and young adults on Snapchat. Data comprised of publicly available tobacco (n = 70) and cannabis-related (n = 64) ads from Snapchat in 2019. Identified themes included: Health consequences (Health effects of tobacco or cannabis use), Financial & legal consequences (Adverse financial or legal implications of substance use), Quitting (Resources for cessation), Industry tactics (Tobacco industry misleads individuals), Policy advocacy (Cannabis law reforms or legalization). Ad performance metrics included average Ad Impressions (number of views per ad) and Ad Spend (cost per ad). Ads were also categorized by Sponsoring Organizations (Government or Advocacy organizations). Health Consequences was the predominant theme followed by Quitting among tobacco-related ads. Government organizations sponsored most tobacco-related ads. Tobacco-related ads targeting adolescents received mean = 4,122,071 impressions and cost mean = $10,385.6 per ad. Tobacco-related ads targeting young adults received mean = 2,151,217 impressions and cost mean = $5,382.1 per ad. Health Consequences was a predominant theme among cannabis-related ads followed by Policy Advocacy. Advocacy organizations sponsored most cannabis-related ads targeting young adults. Cannabis-related ads targeting adolescents received mean = 415,293.8 impressions and cost mean=$793.92 per ad. Cannabis-related ads targeting young adults received mean = 293,267.7 impressions, and cost mean = $740.58. Government and advocacy organization sponsored ads reached millions of adolescents and young adults on Snapchat. Prevention campaigns may consider these number of impressions and cost per ad by theme when designing platform specific ads in the future.

Background: Emerging evidence suggests that exposure to secondhand and thirdhand aerosol from electronic cigarettes may have serious health risks including respiratory and cardiovascular diseases. Social media data can help identify common locations referenced in vaping-related discussions and offer clues about where individuals vape. These insights can strengthen current tobacco regulations and prioritize new policies to improve public health. This study identified commonly referenced locations in vaping-related discussions on Twitter in 2018. Methods: Vaping-related posts to Twitter were obtained from 1 January 2018 to 31 December 2018. Rule-based classifiers categorized each Twitter post into 11 location-related categories (social venues, living spaces, stores, modes of transportation, schools, workplaces, healthcare offices, eateries, correctional facilities, religious institutions, and miscellaneous) using a data dictionary of location-related keywords (n = 290,816). Results: The most prevalent category was social venues (17.9%), followed by living spaces (16.7%), stores (15.9%), modes of transportation (15.5%), schools (14.9%), and workplaces (11.9%). Other categories pertained to: healthcare offices (2.0%), eateries (1.2%), correctional facilities (0.7%), and religious institutions (0.4%). Conclusion: This study suggests that locations related to socialization venues may be priority areas for future surveillance and enforcement of smoke-free air policies. Similarly, development and enforcement of similar policies at workplaces, schools and multi-unit housing may curb exposure to secondhand and thirdhand aerosol among the public.

Vape shops sell electronic cigarettes and related products such as e-liquids, which may contain nicotine. Direct contact with nicotine can lead to adverse health effects, and few regulations exist on how nicotine is handled in vape shops. This study examined how customers and employees come into contact with, and handle, nicotine-containing e-liquids in vape shops with the goal of informing potential future regulation of nicotine handling in vape shops. Data were collected from 77 vape shops in the Los Angeles basin. Characteristics of the shops were documented by employee interviews and in store observations. Data collection was focused on shops located in areas with high concentrations of communities of interest; 20 shops from African-American communities, 17 from Hispanic communities, 18 from Korean communities, and 22 from non-Hispanic White communities. Half of the vape shops allowed customers to sample e-liquids with nicotine. Most of the shops (83%) provided self-service sampling stations for customers. A majority of shop employees (72%) reported that spills of e-liquids containing nicotine had occurred in the past. While 64% of the shops provided safety equipment, only 34% provided equipment for proper nicotine handling. Furthermore, 62% of shop employees reported handling nicotine without gloves or other safety equipment. Regulation on the handling of nicotine by customers and vape shop employees is important to prevent unsafe practices and subsequent injury. The frequent occurrence of spills and limited availability of safety equipment in vape shops highlights the need for the creation and enforcement of regulations to protect employees and customers. Appropriate safety training and equipment should be provided to employees to prevent accidental exposure to nicotine. Information on ways to safely handle nicotine should be communicated to vape shop employees and customers.