Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

BACKGROUND: An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. OBJECTIVE: The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. METHODS: Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. RESULTS: Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). CONCLUSIONS: Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter.

Amid a potential menthol ban, electronic cigarette (e-cigarette) companies are incorporating synthetic cooling agents like WS-3 and WS-23 to replicate menthol/mint sensations. This study examines public views on synthetic cooling agents in e-cigarettes via Twitter data. From May 2021 to March 2023, we used Twitter Streaming Application Programming Interface (API), to collect tweets related to synthetic cooling agents with keywords such as 'WS-23,' 'ice,' and 'frozen.' The deep learning RoBERTa (Robustly Optimized BERT-Pretraining Approach) model that can be optimized for contextual language understanding was used to classify attitudes expressed in tweets about synthetic cooling agents and identify e-cigarette users. The BERTopic (a topic modeling technique that leverages Bidirectional Encoder Representations from Transformers) deep-learning model, specializing in extracting and clustering topics from large texts, identified major topics of positive and negative tweets. Two proportion Z-tests were used to compare the proportions of positive and negative attitudes between e-cigarette users (vapers) and non-e-cigarette-users (non-vapers). Of 6,940,065 e-cigarettes-related tweets, 5,788 were non-commercial tweets related to synthetic cooling agents. The longitudinal trend analysis showed a clear upward trend in discussions. Vapers posted most of the tweets (73.05%, 4,228/5,788). Nearly half (47.87%, 2,771/5,788) held a positive attitude toward synthetic cooling agents, which is significantly higher than those with a negative attitude (19.92%,1,153/5,788) with a P-value < 0.0001. The likelihood of vapers expressing positive attitudes (60.17%, 2,544/4,228) was significantly higher (P < 0.0001) than that of non-vapers (14.55%, 227/1,560). Conversely, negative attitudes from non-vapers (30%, 468/1,560) were significantly (P < 0.0001) higher than vapers (16.2%, 685/4,228). Prevalent topics from positive tweets included "enjoyment of specific vape flavors," "preference for lush ice vapes," and "liking of minty/icy feelings." Major topics from negative tweets included "disliking certain vape flavors" and "dislike of others vaping around them." On Twitter, vapers are more likely to have a positive attitude toward synthetic cooling agents than non-vapers. Our study provides important insights into how the public perceives synthetic cooling agents in e-cigarettes. These insights are crucial for shaping future U.S. Food and Drug Administration (FDA) regulations aimed at safeguarding public health.

How to Identify e-Cigarette Brands Available in the United States During 2020-2022: Development and Usability Study.

BACKGROUND: Prior studies have demonstrated that the e-cigarette market contains a large number of brands. Identifying these existing e-cigarette brands is a key element of market surveillance, which will further assist in policy making and compliance checks. OBJECTIVE: To facilitate the surveillance of the diverse product landscape in the e-cigarette market, we constructed a semantic database of e-cigarette brands that have appeared in the US market as of 2020-2022. METHODS: In order to build the brand database, we searched and compiled e-cigarette brands from a comprehensive list of retail channels and sources, including (1) e-liquid and disposable brands sold in web-based stores, (2) e-cigarette brands sold in brick-and-mortar stores and collected by the Nielsen Retail Scanner Data, (3) e-cigarette brands compiled by Wikipedia, (4) self-reported e-cigarette brands from the 2020 International Tobacco Control Four-Country Smoking and Vaping (ITC 4CV) US survey, and (5) e-cigarette brands on Twitter. We also estimated the top 5 e-cigarette brands by sales volume in brick-and-mortar stores, by the frequency and variety of offerings in web-based shops, and by the frequency of self-reported brands from the 2020 ITC 4CV US survey. RESULTS: As of 2020-2022, a total of 912 e-cigarette brands have been sold by various retail channels. During 2020-2022, the top 5 brands are JUUL, vuse, njoy, blu, and logic in brick-and-mortar stores; blu, king, monster, twist, and air factory for e-liquids in web-based stores; hyde, pod mesh, suorin, vaporlax, and xtra for disposables sold in web-based stores; and smok, aspire, vaporesso, innokin, and eleaf based on self-reported survey data. CONCLUSIONS: As the US Food and Drug Administration enforces the premarket tobacco market authorization, many e-cigarette brands may become illegal in the US market. In this context, how e-cigarette brands evolve and consolidate in different retail channels will be critical for understanding the regulatory impacts on product availability. Our semantic database of e-cigarette brands can serve as a useful tool to monitor product and marketplace development, conduct compliance checks, assess manufacturers' marketing behaviors, and identify regulatory impacts.

Abnormal expression profile of plasma exosomal microRNAs in exclusive electronic cigarette adult users.

Background: Exposure to electronic cigarette (e-cigarette) aerosol has been linked to several health concerns, including DNA damage, elevated oxidative stress, the release of inflammatory cytokine, and dysfunctions in epithelial barriers. However, little is known about the effect of exclusive e-cigarette use on expression profiles of exosomal miRNAs, which play critical regulatory roles in many inflammatory responses and disease processes including cancer. We aim to compare the exosomal microRNA expression profile between exclusive e-cigarette users and normal controls without any tobacco product use (non-users). Methods: Using plasma samples from 15 exclusive e-cigarette users and 15 non-users in the Population Assessment of Tobacco and Health (PATH) Wave 1 study (2013-2014), we examined exosomal microRNAs expression levels through Illumina NextSeq 500/550 sequencing. The differential analyses between exclusive e-cigarette users and non-users were examined using the generalized linear model approach in the DESeq2 package in R/Bioconductor after adjusting the significant confounding effect from race. Gene enrichment analyses were conducted on target genes regulated by significant microRNAs in the differential analyses. Further, molecular-based techniques using the micro RNA mimics and inhibitors were applied for the validation of the expressions of the micro RNAs in vitro. Results: We identified four microRNAs that have significantly higher expression levels in exclusive e-cigarette users than non-users including hsa-miR-100-5p, hsa-miR-125a-5p, hsa-miR-125b-5p, and hsa-miR-99a-5p. GO enrichment analysis on the target genes regulated by the four microRNAs showed that dysregulation of the four microRNAs in exclusive e-cigarette users involved in multiple cell processes such as protein kinase binding and miRNA metabolic process. KEGG pathway enrichment analysis found the four upregulated miRNAs in exclusive e-cigarette users involved in many cancer pathways such as the non-small cell lung cancer, small cell lung cancer, pancreatic cancer, p53 signaling pathway, Hippo signaling pathway, HIF-1 signaling pathway, and MAPK signaling pathway. Overexpression of miRNA hsa-miR-125b-5p was shown to promote DNA damage in bronchial epithelia cells. Conclusions: Four plasma exosomal microRNAs involved in cancer development had higher expression levels in exclusive e-cigarette users than non-users, which might indicate a potentially elevated risk of cancer among exclusive e-cigarette users.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

INTRODUCTION: Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence. AIMS AND METHODS: We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts. RESULTS: Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts. CONCLUSION: Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use. IMPLICATIONS: Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Public perceptions of the FDA's marketing authorization of Vuse on Twitter/X.

Introduction: On October 12, 2021, the FDA issued its first marketing granted orders for Vuse, the e-cigarette product by R.J. Reynolds Vapor Company. The public perceptions and reactions to the FDA's Vuse authorization are prevalent on social media platforms such as Twitter/X. We aim to understand public perceptions of the FDA's Vuse authorization in the US using Twitter/X data. Methods: Through the Twitter/X streaming API (Application Programming Interface), 3,852 tweets between October 12, 2021, and October 23, 2021, were downloaded using the keyword of Vuse. With the elimination of retweets, irrelevant tweets, and tweets from other countries, the final dataset consisted of 523 relevant tweets from the US. Based on their attitudes toward the FDA authorization on Vuse, these tweets were coded into three major categories: positive, negative, and neutral. These tweets were further manually classified into different categories based on their contents. Results: There was a large peak on Twitter/X mentioning FDA's Vuse authorization on October 13, 2021, just after the authorization was announced. Of the 523 US tweets related to FDA's Vuse authorization, 6.12% (n=32) were positive, 26.77% (n=140) were negative, and 67.11% (n=351) were neutral. In positive tweets, the dominant subcategory was Cessation Claims (n=18, 56.25%). In negative tweets, the topics Health Risk (n=43, 30.71%), Criticize Authorization (n=42, 30.00%), and Big Tobacco (n=40, 38.57%) were the major topics. News (n=271, 77.21%) was the most prevalent topic among neutral tweets. In addition, tweets with a positive attitude tend to have more likes. Discussion: Public perceptions and discussions on Twitter/X regarding the FDA's Vuse authorization in the US showed that Twitter/X users were more likely to show a negative than a positive attitude with a major concern about health risks.

Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter.

Amid a potential menthol ban, electronic cigarette (e-cigarette) companies are incorporating synthetic cooling agents like WS-3 and WS-23 to replicate menthol/mint sensations. This study examines public views on synthetic cooling agents in e-cigarettes via Twitter data. From May 2021 to March 2023, we used Twitter Streaming API, to collect tweets related to synthetic cooling agents with keywords such as 'WS-23,' 'ice,' and 'frozen.' Two deep learning roBERTa models, classified attitudes expressed in tweets about synthetic cooling agents and identified e-cigarette users. The BERTopic deep-learning model identified major topics of positive and negative tweets. Two proportion Z-tests were used to compare the proportions of positive and negative attitudes between e-cigarette users (vapers) and non-e-cigarette-users (non-vapers). Of 6,940,065 e-cigarettes related tweets, 5,788 non-commercial tweets related to synthetic cooling agents. The longitudinal trend analysis showed a clear upwards trend in discussions. Vapers posted most of the tweets (73.05%, 4,228/5,788). Nearly half (47.87%, 2,771/5,788) held a positive attitude toward synthetic cooling agents, which is significantly higher than those with a negative attitude (19.92%,1,153/5,788) with a P-value < 0.0001. The likelihood of Vapers expressing positive attitudes (60.17%, 2,544/4,228) was significantly higher (P < 0.0001) than that from non-vapers (14.55%, 227/1,560). Conversely, negative attitudes from non-vapers (30%, 468/1,560) was significantly (P < 0.0001) higher than vapers (16.2%, 685/4,228). Prevalent topics from positive tweets included "enjoyment of specific vape flavors," "preference for lush ice vapes," and "liking of minty/icy feelings." Major topics from negative tweets included "disliking certain vape flavors" and "dislike of others vaping around them." On Twitter, vapers are more likely to have a positive attitude toward synthetic cooling agents than non-vapers. Our study provides important insights into how the public perceives synthetic cooling agents in e-cigarettes, insights that are crucial for shaping future FDA regulations aimed at safeguarding public health.

Public Perceptions and Discussions of Premium Cigars on Reddit.

Introduction: While premium cigars have similar addictive, toxic, and carcinogenic constituents as other cigars and cigarettes, about 1% of the US adults reported premium cigar use from 2010 to 2019. This study aimed to understand public perceptions and discussions of premium cigars on Reddit, one of the most popular social media platforms. Methods: Using keywords such as "premium cigar", we extracted 2,238 Reddit posts from Reddit Archive between July 2019 and June 2021. Among them, 1,626 posts were related to premium cigars. By employing the inductive approach, we manually coded each Reddit post on premium cigars to understand public perceptions and discussions of premium cigars by summarizing them into different topics and subtopics. Results: Longitudinal analysis showed that the number of Reddit posts on premium cigars increased since June 2020. Content analysis showed that among Reddit posts related to premium cigars, the most popular topic is "Information sharing" (75.72%), in which Reddit users shared their perceptions about premium cigars, asked for advice, and provided some recommendations about premium cigars. Over one-quarter of posts (27.17%) are sharing user experiences of premium cigars (such as taste). Nearly one-fifth (18.99%) of posts are discussing the affordability of premium cigars. In addition, 7.87% of posts are discussing legal/policy issues related to premium cigars, and 6.82% of posts are related to the health risks of premium cigars compared to cigarettes. Conclusions: Public perceptions including misperceptions, user experiences, and affordability related to premium cigars have been actively discussed on Reddit.

Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study.

Background: Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. Objective: In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. Methods: We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as "waterpipe," "hookah," and "shisha." We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe-related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe-related posts before and during the pandemic. Results: A total of 45,765 nonpromotion water pipe-related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word "home" remained similar during the COVID-19 pandemic (P=.29). While water pipe-related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe-related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. Conclusions: This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit.

Public Perceptions of Flavored Waterpipe Smoking on Twitter.

Waterpipe tobacco smoking has become increasingly popular in recent years, especially among youth. We aimed to understand longitudinal trends in the prevalence and user perception of waterpipes and their flavors on Twitter. We extracted waterpipe-related tweets from March 2021 to May 2022 using the Twitter Streaming API and classified them into promotional tweets and non-promotional tweets. We examined the longitudinal trends regarding the waterpipe flavors mentioned on Twitter and conducted sentiment analysis on each waterpipe flavor-related non-promotional tweet. Among over 1.3 million waterpipe-related tweets, 1,158,884 tweets were classified as non-promotional and 235,132 were classified as promotional. The most frequently mentioned waterpipe flavor groups were fruit (34%), sweets (17%), and beverages (15%) among all flavor-containing non-promotional tweets (17,746 tweets). The least mentioned flavor groups were tobacco (unflavored, 4%) and spices (2%). Sentiment analysis showed that among non-promotional waterpipe-related tweets, 39% were neutral, 36% were positive, and 23% were negative. The most preferred waterpipe flavors were fruit, mixed, and alcohol flavors. The least preferred flavor groups were tobacco and spice flavors. Our study provided valuable information on the prevalence of waterpipe flavors that can be used to support the future regulation of flavored waterpipe tobacco products given the nature of the current regulations on other flavored tobacco products.

Characterizing e-Cigarette-Related Videos on TikTok: Observational Study.

BACKGROUND: As a popular social networking platform for sharing short videos, TikTok has been widely used for sharing e-cigarettes or vaping-related videos, especially among the youth. OBJECTIVE: This study aims to characterize e-cigarette or vaping-related videos and their user engagement on TikTok through descriptive analysis. METHODS: From TikTok, a total of 417 short videos, posted between October 4, 2018, and February 27, 2021, were collected using e-cigarette or vaping-related hashtags. Two human coders independently hand-coded the video category and the attitude toward vaping (provaping or antivaping) for each vaping-related video. The social media user engagement measures (eg, the comment count, like count, and share count) for each video category were compared within provaping and antivaping groups. The user accounts posting these videos were also characterized. RESULTS: Among 417 vaping-related TikTok videos, 387 (92.8%) were provaping, and 30 (7.2%) were antivaping videos. Among provaping TikTok videos, the most popular category is vaping tricks (n=107, 27.65%), followed by advertisement (n=85, 21.95%), customization (n=75, 19.38%), TikTok trend (n=70, 18.09%), others (n=44, 11.37%), and education (n=6, 1.55%). By comparison, videos showing the TikTok trend had significantly higher user engagement (like count per video) than other provaping videos. Antivaping videos included 15 (50%) videos with the TikTok trend, 10 (33.33%) videos on education, and 5 (16.67%) videos about others. Videos with education have a significantly lower number of likes than other antivaping videos. Most TikTok users posting vaping-related videos are personal accounts (119/203, 58.62%). CONCLUSIONS: Vaping-related TikTok videos are dominated by provaping videos focusing on vaping tricks, advertisement, customization, and TikTok trend. Videos with the TikTok trend have higher user engagement than other video categories. Our findings provide important information on vaping-related videos shared on TikTok and their user engagement levels, which might provide valuable guidance on future policy making, such as possible restrictions on provaping videos posted on TikTok, as well as how to effectively communicate with the public about the potential health risks of vaping.

An evaluation of statistical differential analysis methods in single-cell RNA-seq data.

Background: Single-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data. Results: Through simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing data. The five methods included DEsingle (Zero-inflated negative binomial model), Linnorm (Empirical Bayes method on transformed count data using the limma package), monocle (An approximate Chi-Square likelihood ratio test), MAST (A generalized linear hurdle model), and DESeq2 (A generalized linear model with empirical Bayes approach and also commonly used for bulk RNA sequencing differential express analyses). We assessed the false discovery rate (FDR) control, sensitivity, specificity, accuracy, and area under the receiver operating characteristics (AUROC) curve for all five methods under different sample sizes, distribution assumptions, and proportions of zeros in the data. Conclusions: We found the MAST method performed the best among the five methods compared with the largest AUROC values across all tested sample sizes and different proportion of truly differential expressed genes, when the data followed negative binomial distributions. When the sample size increased to 100 in each group, the MAST method showed the best performance with the highest AUROC regardless of the data distributions. If the excess zeros were first filtered out before the gene differential analyses, the DESingle, Linnorm, and DESeq2 performed relatively better than the MAST and the monocle methods with higher AUROC values.

Characterizing Heated Tobacco Products Marketing on Instagram: Observational Study.

BACKGROUND: Heated tobacco products (HTPs), including I Quit Ordinary Smoking (IQOS), are new tobacco products that use an electronic device to heat compressed tobacco leaves to generate an aerosol for consumers to inhale. Marketing of HTPs is prevalent on Instagram, a popular social media platform. OBJECTIVE: This study aims to characterize posts related to HTPs on Instagram and their associations with user engagement. METHODS: Through the Instagram application programming interface, 979 Instagram posts were collected using keywords related to HTPs, such as "IQOS" and "heat-not-burn." Among them, 596 posts were related to IQOS and other HTP marketing. The codebook was developed from a randomly selected 200 posts on the post content by hand coding, which was applied to the remaining 396 Instagram posts. Summary statistics were calculated, and statistical hypothesis testing was conducted to understand the popularity of Instagram posts on HTPs. Negative binomial regression models were applied to identify Instagram post characteristics associated with user engagement (eg, count). RESULTS: Among Instagram posts related to HTP marketing (N=596), "product display" was dominant (n=550, 92.28%), followed by "brand promotion" (n=41, 6.88%), and "others" (n=5, 0.84%). Among posts within "product display," "device only" was the most popular (n=338, 61.45%), followed by "heatstick only" (n=80, 14.55%), "accessory" (n=66, 12%), "device and heatstick" (n=56, 10.18%), and "capsule" (n=10, 1.82%). A univariate negative binomial regression model with pairwise comparisons across "product display" types showed that the number of likes for posts with HTP heatsticks was significantly lower compared to posts with HTP devices, accessories, and device-heatstick sets. Multivariate negative binomial regression models showed that HTP-related Instagram posts with a model or lifestyle elements (;=.60, 95% CI 0.36-0.84) or without obvious product advertising information (=.69, 95% CI 0.49-0.89) received more likes. CONCLUSIONS: It is shown that posts with product displays were dominant among HTP-related posts on Instagram. Posts with model or lifestyle elements are associated with high user engagement, which might be one of the web-based marketing strategies of HTPs.

Public Perceptions of the Food and Drug Administration's Proposed Rules Prohibiting Menthol Cigarettes on Twitter: Observational Study.

BACKGROUND: On April 28, 2022, the Food and Drug Administration (FDA) proposed rules that prohibited all menthol-flavored cigarettes and other flavored cigars to prevent the initiation of tobacco use in youth and reduce tobacco-related diseases and death. OBJECTIVE: The objective of this study was to investigate public perceptions of the FDA's proposed menthol cigarette rules on Twitter. METHODS: Through the Twitter streaming application programming interface, tobacco-related tweets were collected between April 28, 2022, and May 27, 2022, using a set of keywords, such as smoking, cigarette, and nicotine. Furthermore, 1941 tweets related to the FDA's proposed menthol cigarette rules were extracted. Based on 300 randomly selected example tweets, the codebook for the attitudes toward the FDA's proposed rules and related topics was developed by 2 researchers and was used to label the rest of the tweets. RESULTS: Among tweets related to the FDA's proposed menthol cigarette rules, 536 (27.61%) showed a positive attitude, 443 (22.82%) had a negative attitude, and 962 (49.56%) had a neutral attitude toward the proposed rules. Social justice (210/536, 39%) and health issues (117/536, 22%) were two major topics in tweets with a positive attitude. For tweets with a negative attitude, alternative tobacco or nicotine products (127/443, 29%) and racial discrimination (84/536, 16%) were two of the most popular topics. CONCLUSIONS: In general, the public had a positive attitude toward the FDA's proposed menthol cigarette rules. Our study provides important information to the FDA on the public perceptions of the proposed menthol cigarette rules, which will be helpful for future FDA regulations on menthol cigarettes.

Public Perceptions on the Policy of Electronic Cigarettes as Medical Products on Twitter.

Starting from 1 October 2021, Australia requires a prescription for purchasing nicotine vaping products. On 29 October 2021, the UK provided a guideline to treat e-cigarettes as medical products. This study aims to understand public perceptions of the prescription policy in Australia and the UK on Twitter. Tweets related to e-cigarettes from 20 September 2021 to 31 December 2021 were collected through Twitter streaming API. We adopted both a human and machine learning model to identify a total of 1795 tweets from the UK and Australia related to the prescription policy. We classified them into pro-policy, anti-policy, and neutral-to-policy groups, and further characterized tweets into different topics. Compared to Australia, the proportion of pro-policy tweets in the UK was significantly higher (19.43% vs. 10.92%, p < 0.001), while the proportion of anti-policy tweets was significantly lower (43.4% vs. 50.09%, p = 0.003). The main topics for different attitudes towards the prescription policy between the two countries showed some significant differences, for example, "help quit smoking" in the UK and "health effect of e-cigarettes" in Australia for the positive attitude, "economic effect" in the UK and "preventing smoking cessation" in Australia for the negative attitude, which reflected different public concerns. The findings might provide valuable guidance for other countries to implement a similar policy in the future.

Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study.

BACKGROUND: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. OBJECTIVE: This study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. METHODS: We collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette-related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. RESULTS: Through our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. CONCLUSIONS: Our proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.

Background: During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns. Methods: COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic. Results: We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns. Conclusions: The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Impact of the FDA flavour enforcement policy on flavoured electronic cigarette use behaviour changes.

INTRODUCTION: This study aims to investigate electronic cigarette (e-cigarette) use behaviour changes after the implementation of the US Food and Drug Administration (FDA) restriction on the sale of all unauthorised flavoured cartridge-based e-cigarettes other than tobacco and menthol flavour on 6 February 2020, as well as factors associated with these changes. METHODS: Through Amazon's Mechanical Turk service, 3533 current adult flavoured e-cigarette users (who were not exclusive tobacco-flavoured or menthol-flavoured e-cigarette users) were recruited for an online survey from 8 July to 29 July 2021. Multiple logistic regression models were used to identify significant factors associated with quitting e-cigarette use, switching to other flavoured electronic nicotine delivery system (ENDS) products, switching to combustible tobacco products, switching to menthol-flavoured e-cigarettes and switching to tobacco-flavoured e-cigarettes. RESULTS: Resulting from the FDA flavour enforcement policy, the top four e-cigarette use behaviour changes were: (1) switching to other flavoured ENDS products such as the tank system or disposable e-cigarettes (29.24%), (2) switching to menthol-flavoured pod systems (18.09%), (3) switching to combustible tobacco products (14.12%) and (4) switching to tobacco-flavoured pod systems (12.03%). There were 4.9% participants who indicated that they quit e-cigarette use. Overall, multiple factors, especially past 30-day use of certain flavours, were associated with different behaviour changes. CONCLUSIONS: The implementation of the FDA flavour enforcement policy on cartridge-based e-cigarette was associated with significant e-cigarette behaviour changes, with multiple factors being associated with these changes. These results provide important information for future regulations of flavoured e-cigarette products.

Potential Impact of FDA Flavor Enforcement Policy on Vaping Behavior on Twitter.

In January 2020, the FDA announced an electronic cigarette (e-cigarette) flavor enforcement policy to restrict the sale of all unauthorized cartridge-based flavored e-cigarettes except tobacco and menthol flavors, which was implemented on 6 February 2020. This study aimed to understand the potential influence of this policy on one vaping behavior change-quitting vaping-using Twitter data. Twitter posts (tweets) related to e-cigarettes were collected between June 2019 and October 2020 through a Twitter streaming API. Based on the geolocation and keywords related to quitting vaping, tweets mentioning quitting vaping from the US were filtered. The demographics (age and gender) of Twitter users who mentioned quitting vaping were further inferred using a deep learning algorithm (deepFace). The proportion of tweets and Twitter users mentioning quitting vaping were compared between before and after the announcement and implementation of the flavor policy. Compared to before the FDA flavor policy, the proportion of tweets (from 0.11% to 0.20% and 0.24%) and Twitter users (from 0.15% to 0.70% and 0.86%) mentioning quitting vaping were significantly higher after the announcement and implementation of the policy (p-value < 0.001). In addition, there was an increasing trend in the proportion of female and young adults (18-35 years old) mentioning quitting vaping on Twitter after the announcement and implementation of the policy compared to that before the policy. Our results showed that the FDA flavor enforcement policy did have a positive impact on quitting vaping on Twitter. Our study provides an initial evaluation of the potential influence of the FDA flavor enforcement policy on user vaping behavior.