Underage JUUL Use Patterns: Content Analysis of Reddit Messages

A qualitative content analysis of cannabis-related discussions on Reddit during the COVID-19 pandemic

Social media has become an increasingly important way to seek and share experiences, support, knowledge, and advice during the COVID-19 pandemic. Reddit, a pseudonymous social media platform, was one way that young people interacted during the pandemic. Our study goals were two-fold: (1) to categorize information sought and provided by users of r/saplings, a subreddit devoted to cannabis use and is often used by young people, and (2) to examine if conversations changed during the COVID-19 pandemic. We extracted 213 randomly selected posts and 2,546 related comments across four time periods (before the pandemic, during the first wave, summer, and next fall). We assessed the volume of posts and comments throughout our study period and conducted a qualitative content analysis. Quantitatively, the findings demonstrated an increase in the number of posts and comments throughout the study period. Given the substantial growth in subreddit activity throughout our study period, Reddit may play an increasingly important role in youth socialization related to cannabis. From the content analysis, we identified three major themes: how to acquire cannabis, how to use cannabis, and associated consequences. Reddit-users prioritized certain content in their posts at different stages of the pandemic. 'Places to acquire' and 'future use' were most common at the beginning of the pandemic, while the theme of 'consequences' and the topic of 'tolerance' became more prominent during the summer months. The comments to these posts were generally thorough and responsive to the post. Nearly all the information came from opinions or personal experiences. Firstly, our findings suggest that young people viewed Reddit as a viable outlet for conversations about cannabis. Secondly, due to the nature of the peer comments and lack of verifiable information being exchanged, misinformation may still circulate and inadvertently worsen the efforts to reduce cannabis-related harm. Interventions that provide understandable and accurate cannabis-related information in accessible formats may increase young people's ability to access and practice harm reduction.

Factors influencing JUUL e-cigarette nicotine vapour-induced reward, withdrawal, pharmacokinetics and brain connectivity in rats: sex matters

Though vaping likely represents a safer alternative to smoking, it is not without risks, many of which are not well understood, especially for vulnerable populations. Here we evaluate the sex- and age-dependent effects of JUUL nicotine vapour in rats. Following passive nicotine vapour exposures (from 59 mg/ml JUUL nicotine pods), rats were evaluated for reward-like behaviour, locomotion, and precipitated withdrawal. Pharmacokinetics of nicotine and its metabolites in brain and plasma and the long-term impact of nicotine vapour exposure on functional magnetic resonance imaging-based brain connectivity were assessed. Adult female rats acquired conditioned place preference (CPP) at a high dose (600 s of exposure) of nicotine vapour while female adolescents, as well as male adults and adolescents did not. Adult and adolescent male rats displayed nicotine vapour-induced precipitated withdrawal and hyperlocomotion, while both adult and adolescent female rats did not. Adult females showed higher venous and arterial plasma and brain nicotine and nicotine metabolite concentrations compared to adult males and adolescent females. Adolescent females showed higher brain nicotine concentration compared to adolescent males. Both network-based statistics and between-component group connectivity analyses uncovered reduced connectivity in nicotine-exposed rats, with a significant group by sex interaction observed in both analyses. The short- and long-term effects of nicotine vapour are affected by sex and age, with distinct behavioural, pharmacokinetic, and altered network connectivity outcomes dependent on these variables.

Online platforms' framing around vaping

In this paper, we provide a descriptive overview of how vaping is framed differently between various online platforms (Wikipedia, Quora, Medium, Reddit, Stack Exchange, wikiHow, Facebook, and online news media). We provide an overview of >1 million posts and news articles about vaping to study the differences in framing between online platforms. Findings indicate an inconsistent framing around vaping across platforms. Stakeholders may utilize our findings to intervene around the framing of vaping and may design communications campaigns that improve the way society sees vaping, possibly aiding smoking cessation and reducing youth vaping.

An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit

Introduction

This study analyzes age-differentiated Reddit conversations about ENDS.

Methods

This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded.

Results

Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior.

Conclusions

Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.

Discussions of Flavored ENDS Sales Restrictions: Themes Related to Circumventing Policies on Reddit

OBJECTIVE

To examine conversations among JUUL users on Reddit related to restrictions on flavored ENDS and the shifting policy landscape.

METHODS

Posts and comments (n = 166,169) between May 2019 and May 2020 on the subreddit r/JUUL were scraped using pushshift.io API. Keyword filters were used to identify texts discussing flavored ENDS products (n = 33,884 texts). These were further narrowed down to texts discussing flavor policy workaround strategies (n = 7429) and N-gram analysis was performed. Finally, findings from the N-gram analysis were triangulated through qualitative review of a separate sample of texts (n = 488) from the flavor policy-related posts and comments.

RESULTS

Overall activity on the subreddit r/JUUL peaked around the time of the EVALI outbreak (September 2019) and when FDA issued guidance restricting flavored ENDS product sales (January 2020). The N-gram analysis revealed an active discussion of banned products one can "still get" or "JUUL compatible" alternatives, including specific brands, brick and mortar locations, and specific flavors. Ten dominant themes emerged from the qualitative review, with some posts containing more than one theme.

CONCLUSION

Many users turned to Reddit for information related to the shifting regulatory landscape concerning flavored ENDS. Discussions focused on both legal alternatives to banned products as well as illegal means of acquiring JUUL pods, including residual retail supply, online, and mail vendors.

Reddit discussions about buprenorphine associated precipitated withdrawal in the era of fentanyl

BACKGROUND

Induction of buprenorphine, an evidence-based treatment for opioid use disorder (OUD), has been reported to be difficult for people with heavy use of fentanyl, the most prevalent opioid in many areas of the country. In this population, precipitated opioid withdrawal (POW) may occur even after individuals have completed a period of opioid abstinence prior to induction. Our objective was to study potential associations between fentanyl, buprenorphine induction, and POW, using social media data.

METHODS

This is a mixed methods study of data from seven opioid-related forums (subreddits) on Reddit. We retrieved publicly available data from the subreddits via an application programming interface, and applied natural language processing to identify subsets of posts relevant to buprenorphine induction, POW, and fentanyl and analogs (F&A). We computed mention frequencies for keywords/phrases of interest specified by our medical toxicology experts. We further conducted manual, qualitative, and thematic analyses of automatically identified posts to characterize the information presented. Results: In 267,136 retrieved posts, substantial increases in mentions of F&A (3 in 2013 to 3870 in 2020) and POW (2 in 2012 to 332 in 2020) were observed. F&A mentions from 2013 to 2021 were strongly correlated with mentions of POW (Spearman's ρ: 0.882; p = .0016), and mentions of the Bernese method (BM), a microdosing induction strategy (Spearman's ρ: 0.917; p = .0005). Manual review of 384 POW- and 106 BM-mentioning posts revealed that common discussion themes included "specific triggers of POW" (55.1%), "buprenorphine dosing strategies" (38.2%) and "experiences of OUD" (36.1%). Many reported experiencing POW despite prolonged opioid abstinence periods, and recommended induction via microdosing, including specifically via the BM.

CONCLUSIONS

Reddit subscribers often associate POW with F&A use and describe self-managed buprenorphine induction strategies involving microdosing to avoid POW. Further objective studies in patients with fentanyl use and OUD initiating buprenorphine are needed to corroborate these findings.HIGHLIGHTSIncrease in mentions of precipitated opioid withdrawal (POW) on Reddit from 2012 to 2021 was associated with the increase in fentanyl and analog mentions.Experiences of precipitated opioid withdrawal (POW) were described by individuals despite reporting prolonged periods of abstinence compared to standard buprenorphine induction protocols.People with Opioid Use Disorder (OUD) on Reddit are using and recommending microdosing strategies with buprenorphine to avoid POW.People who used fentanyl report experiencing POW following statistically longer periods of abstinence than people who use heroin.

Can social media monitoring help identify the next EVALI? An examination of Reddit posts about vitamin E acetate and Dank Vapes

BACKGROUND

Scientists identified vitamin E acetate (VEA) and "Dank Vapes" (a fake brand of tetrahydrocannabinol [THC] vaping products) as contributors to the 2019-2020 outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). On social media, people who post about vaping or THC discussed the causes of EVALI. We examined whether Reddit conversations may have served as early signals of the outbreak.

METHODS

We collected Reddit posts from March 2018 to February 2020 on vaping- and THC-related subreddits that mentioned VEA or Dank Vapes. We identified peaks in post volume, examined post content, and used natural language processing to identify terms most characteristic of posts.

RESULTS

There were almost no posts about VEA before EVALI. Subsequently, there were two peaks, both referencing media coverage of scientific findings that linked VEA to EVALI. Discussion regularly referenced concerns about the legitimacy of Dank Vapes before EVALI; peaks in posts were largely unrelated to scientific findings or media coverage of those findings. The terms most characteristic of VEA posts were EVALI-related; those most characteristic of Dank Vapes posts were about quality or legitimacy.

CONCLUSIONS

Although posts about VEA and Dank Vapes did not predict the outbreak, the public health community could use social media to encourage people who vape or use THC to report future health concerns (e.g., through FDA's Safety Reporting Portal). Researchers and regulators could also use social media to see if potentially problematic products, such as Dank Vapes, have a history of concern among individuals who use those products.

Thematic Analysis of Reddit Content About Buprenorphine-naloxone Using Manual Annotation and Natural Language Processing Techniques

BACKGROUND

Opioid use disorder (OUD) is a major public health crisis for which buprenorphine-naloxone is an effective evidence-based treatment. Analysis of Reddit data yields detailed information about firsthand experiences with buprenorphine-naloxone that has the potential to inform treatment of OUD.

METHODS

We conducted a thematic analysis of posts about buprenorphine-naloxone from a Reddit forum in which Reddit users anonymously discuss topics related to opioid use. We used an application programming interface to retrieve posts about buprenorphine-naloxone, then applied natural language processing to generate meta-information and curate samples of salient posts. We manually categorized posts according to their content and conducted natural language processing-aided analysis of posts about buprenorphine tapering strategies, withdrawal symptoms, and adjunctive substances/behaviors useful in the tapering process.

RESULTS

A total of 16,146 posts from 1933 redditors were retrieved from the /r/suboxone subreddit. Thematic analysis of sample posts (N = 200) revealed descriptions of personal experiences (74%), nonpersonal accounts (24%), and other content (2%). Among redditors who reported tapering to termination (N = 40), 0.063 mg and 0.125 mg were the most common termination doses. Fatigue, gastrointestinal disturbance, and mood disturbance were the most frequent adverse effects, and loperamide and vitamins/dietary supplements the most frequently discussed adverse effects adjunctive substances/behaviors respectively.

CONCLUSIONS

Discussions on Reddit are rich in information about buprenorphine-naloxone. Information derived from analysis of Reddit posts about buprenorphine-naloxone may not be available elsewhere and may help providers improve treatment of people with OUD through better understanding of the experiences of people who have used buprenorphine-naloxone.

Conceptual model for the evaluation of attractiveness, addictiveness and toxicity of tobacco and related products: The example of JUUL e-cigarettes

Many new tobacco and related products (nTRP) have emerged on the market, with unknown health risks. Here, we present a conceptual model containing the factors and relations between them that contribute to the nTRP's health effects. Factors that determine attractiveness, addictiveness and toxicity of nTRP were defined based on previous assessments, literature, and expert discussions. Our model will aid in identifying key risk factors contributing to increased risk of adverse health effects for a product in a qualitative manner. Additionally, it can gauge attractiveness for specific user groups, as a determinant for population prevalence of use. Our model can be used to identify aspects of nTRP that require attention for public information or product regulation. As an example, we applied this to JUUL, a popular e-cigarette in the US. Aspects of concern for JUUL are its attractive and discrete shape, user-friendly prefilled pods, flavors, high aerosol nicotine levels, and liquids containing nicotine salts instead of free-based nicotine. The addictiveness and especially attractiveness are sufficiently high to have a large potential impact on population health due to its contribution to use and hence exposure. Products and their use can change over time; therefore market research and monitoring are crucial.

e-Cigarette Cessation: Content Analysis of a Quit Vaping Community on Reddit

Background

e-Cigarette use, also known as vaping, has increased dramatically over the past few years, especially among younger demographics. However, researchers have found that a large number of e-cigarette users want to quit. Little is known about the unique aspects of vaping cessation, which is critical to informing the development of relevant resources and interventions for e-cigarette users who want to quit. Social media forums such as Reddit provide opportunities to understand the experiences of behavior change such as quitting vaping from the perspective of end users.

Objective

This study aims to examine a quit vaping subreddit to understand how e-cigarette users are experiencing and approaching vaping cessation. Specifically, we examine methods used to approach quitting, reasons for quitting, and barriers and facilitators to quitting.

Methods

A total of 1228 posts were collected. The posts were inductively coded to generate categories and subcategories using conventional content analysis. Data were analyzed using the NVivo 12 qualitative data analysis software.

Results

Most users reported a preference for approaching quitting through gradual reduction, particularly through the use of their own devices by tapering the nicotine content. Their reasons for quitting were primarily related to experiencing negative physical consequences associated with vaping, especially in relation to their lungs (eg, tight chest), and tired of feeling stuck to the vape because of nicotine addiction. Top barriers to quitting were related to withdrawal symptoms and intensity of addiction. The top facilitators to quitting were related to using distraction techniques (eg, hobby, gaming, and mindfulness exercises), as well as having a positive mindset.

Conclusions

The findings of this study reveal unique aspects that encompass the process of quitting vaping. These findings have significant implications for both policy and intervention development.

Peer-to-Peer Social Media Communication About Dietary Supplements Used for Weight Loss and Sports Performance Among Military Personnel: Pilot Content Analysis of 11 Years of Posts on Reddit

Background

Over 60% of military personnel in the United States currently use dietary supplements. Two types of dietary supplements, weight loss and sports performance (WLSP) supplements, are commonly used by military personnel despite the associated serious adverse effects such as dehydration and stroke.

Objective

To understand peer-to-peer communication about WLSP supplements among military personnel, we conducted a pilot study using the social media website, Reddit.

Methods

A total of 64 relevant posts and 243 comments from 2009 to 2019 were collected from 6 military subreddits. The posts were coded for year of posting, subreddit, and content consistent with the following themes: resources about supplement safety and regulation, discernability of supplement use through drug testing, serious adverse effects, brand names or identifiers, and reasons for supplement use.

Results

A primary concern posted by personnel who used supplements was uncertainty about the supplements that were not detectable on a drug test. Supplements to improve workout performance were the most frequently used.

Conclusions

Our pilot study suggests that military personnel may seek out peer advice about WLSP supplements on Reddit and spread misinformation about the safety and effectiveness of these products through this platform. Future directions for the monitoring of WLSP supplement use in military personnel are discussed.

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

BACKGROUND

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.

OBJECTIVE

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.

METHODS

The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.

RESULTS

Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.

CONCLUSIONS

Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation

BACKGROUND

Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users.

OBJECTIVE

We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data.

METHODS

This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature.

RESULTS

The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group.

CONCLUSIONS

We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

"This show hits really close to home on so many levels": An analysis of Reddit comments about HBO's Euphoria to understand viewers' experiences of and reactions to substance use and mental illness

BACKGROUND

Public health has begun using social media forums such as Reddit to enhance surveillance and modernize interventions for young people. The current study's objective was to examine Reddit posts about the HBO series Euphoria to identify show themes that resonate with adolescent and young adult viewers in order to inform future social media interventions.

METHODS

Reddit comments in the r/television community from June to August 2019 were downloaded. Following filtering, 725 comments were analyzed and coded using a codebook and ATLAS.ti. Coded comments were analyzed for themes relevant to Redditor substance use, reactions to Euphoria and the main character (Rue), and mental health concerns.

RESULTS

During their discussion of the show, Redditors disclosed both personal recreational and prescription drug use, including substance use to cope with mental illness symptoms. There were approximately equal numbers of comments with positive and negative reactions to the show overall and to the main character, Rue. Redditors often found Euphoria's storyline and portrayed events to be relatable and realistic to the experience of young people who use drugs, as well as sometimes triggering. Overall, Redditors thought Rue accurately depicted an individual's struggle with a substance use disorder.

CONCLUSIONS

This exploratory study highlights how television and social media can contribute to young peoples' understanding of substance use disorders and mental health. Findings could inform the design of social media interventions for adolescents and young adults on a variety of substance use issues, including stigma and the interconnectedness of substance use and mental health challenges.

What are Ophthalmology Patients Asking Online? An Analysis of the Eye Triage Subreddit

Importance

Ophthalmology patients are seeking medical advice on social media websites like Reddit, where users are able to post comments and discuss issues pertaining to different topics that are organized in ‘subreddits’. Understanding which issues are most pertinent will guide ophthalmic providers in delivering more effective patient education.

Methods

This cross-sectional study assessed a systematic sample of the first 22 posts and their top 3 comments from each month since January 27th, 2019, the subreddit’s creation. Information was gathered from reddit.com/r/eyetriage in October 2019 and analyzed in November 2019.

Main Outcomes

The posts were characterized by date and time, inclusion of an image, type, content, emotional tone, and number of upvotes and comments. The comments were categorized based on content, emotional tone, time of comment, and user background. Post and comment content codes were categorized in an iterative manner with differences resolved by author consensus. Categorical statistics were compiled.

Results

Two hundred posts and 456 comments were analyzed since the creation of r/eyetriage, a forum created exclusively for patients to seek advice from health-care professionals. Twenty-six (13%) of the total posts included an image. On average, comments received 1.76 ± 2.17 upvotes along with 4.50 ± 4.47 replies. The most common content codes among the posts were 42 (21.0%) seeking diagnoses, 23 (11.5%) surgical complications, and 13 (6.50%) alternative medication options. Eighty-two (41%) posts conveyed a clear emotional tone, most notably 11 (13.4%) with anxiety and 10 (12.2%) with worry. The top comments came from 165 (36.2%) self-identified patients, 151 (33.1%) optometrists, and 49 (10.8%) ophthalmologists. The top comment codes for replies included 158 (34.7%) with treatment advice, 70 (15.4%) with advice deferred to follow-up appointment with other health-care specialists, and 60 (13.2%) with sharing information.

Conclusions

Patients are asking ophthalmology-related questions on the Eye Triage subreddit, and they are more likely to receive information from other patients or optometrists than from self-identified ophthalmologists. When emotions were revealed, patients were often anxious and worried. Opportunities exist for ophthalmologists to take a more active role on this subreddit and help educate patients.

Analysis of potential associations of JUUL flavours with health symptoms based on user-generated data from Reddit

BACKGROUND

The rise of the popular e-cigarette, JUUL, has been partly attributed to various teen-friendly e-liquid flavours offered. However, the possible health risks associated with each e-liquid flavour still remain unclear. This research focuses on the possible associations between JUUL flavours and health symptoms using social media data from Reddit.

METHODS

Keyword filtering was used to obtain 5,746 JUUL flavour-related posts and 7927 health symptom-related posts from June 2015 to April 2019 from Reddit. Posts from September 2016 to April 2019 were used to conduct temporal analysis for nine health symptom categories and the 8 JUUL flavours. Finally, associations between the JUUL flavours and health symptom categories were examined on the user level using generalised estimating equation models.

RESULTS

According to our temporal analysis, Mango and Mint were the most discussed JUUL flavours on Reddit. Respiratory and throat symptoms were the most discussed health issues together with JUUL on Reddit over time. Respiratory symptoms had potential associations with the Mango, Mint and Fruit JUUL flavours. Digestive symptoms had a potential association with the Crème flavour, psychological symptoms had a potential association with the Cucumber flavour, and cardiovascular symptoms had a potential association with the tobacco flavours.

CONCLUSIONS

Mango and Mint were the two most mentioned JUUL flavours on Reddit. Certain JUUL flavours were more likely to be mentioned together with certain categories of health symptoms by the same Reddit users. Our findings could prompt further medical research into the health symptoms associated with different e-liquid flavours.

Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits

Background

Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL.

Objective

This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL.

Methods

We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency–inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL.

Results

Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were “WTS,” “want to sell,” “for sale,” and “selling” (154/214, 72.0%). The most common key phrases used in buying threads were “look for/get JUUL/pods” (58/168, 34.5%) and “WTB” (53/168, 31.5%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81%), followed by the delivery service (68/1306, 5.21%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4% (49/201) of users wanted to buy JUULs and 46.6% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed.

Conclusions

There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

Exposure to e-cigarette information and advertising in social media and e-cigarette use in Australia: A mixed methods study

OBJECTIVES

The aim of this study was to explore how e-cigarette users in Australia accessed e-cigarette information and advertising on social media platforms.

METHODS

The mixed-methods study design included a survey and semi-structured interviews. Data were collected from March 2019 to July 2019 in Australia. Interviews were conducted following survey participation. Analysis of survey results examined associations between social media and advertising factors and use of e-cigarettes. Thematic analysis methods were applied to interview responses to explore e-cigarette information search and advertising exposure in different social media channels.

RESULTS

The survey had 185 respondents and 14 participated in the interviews. The average social media use time was 11.4 h/week (SD ± 2.05). A total of 91 (49.2 %) participants sought out relevant information and 104 (56.2 %) were exposed to e-cigarette advertisements on social media platforms. Participants who searched for e-cigarette information on social media were more likely to report past (OR = 5.04; 95 % CI 2.23-11.42; p = 0.001) or current e-cigarette use (OR = 9.27; 95 % CI 4.22-20.34; p = 0.001), compared to participants who had never used e-cigarettes. The same relationship was observed for exposure to e-cigarette advertising. Thematic analysis of interviews revealed the diversity of sources of information for e-cigarettes, the pervasiveness of advertising, and the importance of peer recommendations.

CONCLUSIONS

Australian e-cigarette users seek or are exposed to e-cigarette information on a variety of social media platforms. Access to e-cigarette information was concentrated among e-cigarette users but peer influence may be a risk for uptake among non-smokers.