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BACKGROUND. Twitter provides an opportunity to examine misperceptions about nicotine and addiction as they pertain to electronic nicotine delivery systems (ENDS). The purpose of this study was to systematically examine a sample of ENDS-related tweets that presented information about nicotine or addiction for the presence of potential misinformation. METHODS. A total of 10.1 million ENDS-related tweets were obtained from April 2018 through March 2019 and were filtered for unique tweets containing keywords for nicotine and addiction. A subsample (n=3,116) were human coded for type of account (individual, group, commercial, or news) and presence of potential misinformation. RESULTS. Of tweets that presented ENDS-related nicotine or addiction information (n=904), 41.7% (n=377) contained potential misinformation coded as anti-vaping exaggeration, pro-vaping exaggeration, nicotine is not addictive or is never harmful, or unproven health benefits. CONCLUSIONS. Anti-vaping exaggeration tweets distorted or embellished claims about ENDS nicotine and addiction; pro-vaping exaggeration tweets misinterpreted results from scientific studies. Misinformation that nicotine is not addictive or is never harmful or has unproven health benefits appeared less but are potentially problematic. ENDS-related messaging should be designed to be easily understood by the public and monitored to detect the spread of misinterpretation or misinformation on social media.

Purpose We examined associations between electronic vapor product use (vaping) and violence victimization (adolescent relationship abuse (ARA), youth violence, bullying, and experiencing all three types of violence). Methods Data were drawn from the 2017 National Youth Risk Behavioral Survey. Logistic regression examined associations between vaping and violence victimization, adjusting for sex, age, race, identification as a sexual minority, and use of other substances. Results After adjusting for demographic characteristics, vaping was significantly and positively associated with ARA, youth violence, bullying, and experiencing all three types of violence (ARA: adjusted odds ratio (aOR) 3.0, 95% CI: 2.5–3.5; youth violence: aOR 3.0, CI: 2.4–3.6; bullying: aOR 1.5, CI: 1.4–1.7; all three types of violence: 10, CI: 6.3, 15.8). When use of other substances was added into the model, these associations attenuated, and, for bullying, lost significance. Conclusions Vaping is associated with increased violence victimization, particularly for youth experiencing ARA, youth violence, and polyvictimization. Further longitudinal studies are needed to determine directionality of these associations and guide prevention efforts.

BACKGROUND: The Food and Drug Administration (FDA) has targeted the JUUL nicotine vaporizer as particularly appealing to young people. Initial reports suggest that JUUL is being used in schools and other locations in which cigarette smoking is illegal or discouraged. However, there is little scholarly research documenting this. We aimed to systematically analyze JUUL use themes and sentiment on Twitter. METHODS: Data were collected from Twitter’s Filtered Streams Application Programming Interface from April 12 to May 10, 2018. Excluding re-tweets, this yielded 67,934 tweets, from which a random sample of 2% was selected for coding. After removing irrelevant tweets, the final dataset included 1209 tweets. Inter-rater reliability ranged κ = 0.64–0.85. RESULTS: The majority (71.5%) of tweets expressed positive sentiment toward JUUL, while 14.1% expressed negative sentiment. JUUL use in places where cigarette smoking is illegal or discouraged appeared in 111 tweets (9.2%), with approximately one-third of these tweets referring to using the device in school. Nearly 20% of tweets mentioned using the device at home and/or directly in front of responsible adults. CONCLUSIONS: This study confirms anecdotal reports of JUUL use in places where cigarette smoking is illegal or discouraged. Positive sentiment about use of JUUL in these places suggests that the product is being normalized among young people. It may be valuable for educators and other responsible adults to discuss the addictive nature of nicotine delivered through JUUL with younger populations. This study supports policy implications introduced by the FDA aimed at curbing youth use.

BACKGROUND AND OBJECTIVES Vaping has gained popularity among adolescents despite negative health consequences. Few studies have focused on factors that may protect against vaping. We sought to determine if future orientation, parental monitoring, school connectedness, and social support are associated with decreased risk of vaping and other forms of tobacco use. METHODS Data were obtained via anonymous school-based health behavior surveys among ninth- through 12th-graders in Pittsburgh, PA (n = 2487). Protective factors were assessed through validated Likert scale instruments. The primary outcome was recent (past 30-day) vaping. Additional outcomes included other forms of tobacco use and intention to quit tobacco products. Poisson regression models examined associations between protective factors and vaping and tobacco use outcomes. RESULTS Mean age was 15.7 years, 1446 (58.1%) respondents were female, and 671 youth (27.0%) reported recent vaping. Positive future orientation and high parental monitoring were associated with significantly lower prevalence of recent vaping (adjusted prevalence ratio: 0.84 [95% confidence interval: 0.73–0.97] and adjusted prevalence ratio: 0.73 [95% confidence interval: 0.62–0.85], respectively). There were no significant relationships between social support or school connectedness and vaping. All 4 protective factors were inversely associated with other forms of tobacco use. No factors were significantly associated with intent to quit tobacco products. CONCLUSIONS Findings reveal significant inverse associations between future orientation, parental monitoring, and vaping but no relationship between protective factors and intent to quit tobacco products. Developing interventions to foster protective factors in youth and their parental supports may inform primary prevention efforts to reduce vaping and other tobacco use.

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

BACKGROUND Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices. OBJECTIVE This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes. METHODS Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy. RESULTS Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%). CONCLUSIONS This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

BACKGROUND Misinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use–associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use. OBJECTIVE Twitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages (“tweets”) related to ENDS and COVID-19 to inform public health messaging. METHODS Approximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19–related keywords, resulting in 67,321 original tweets. A 5% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories. RESULTS A total of 1930 (57.3%) tweets were coded as relevant to the research topic. Half (n=1008, 52.2%) of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter (n=479, 24.8%) of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories. CONCLUSIONS Discussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging.

The increasing popularity of the JUUL electronic nicotine delivery system among youth poses several potential public health concerns. Social media can be used to better understand the spread of information related to JUUL. This study examined whether adolescents (age18) are following JUUL's official Twitter account and subsequently sharing (retweeting) JUUL's posts to their followers. We also assessed various patterns in which adolescents share information on JUUL with each other.We collected 3,239 tweets from JUUL's official Twitter account (@JUULVapor) for one full year from February 2017 to January 2018. These tweets were shared by Twitter users to their followers 1,124times by 721 unique users. Users were determined to be either adolescent (age18) or adult (≥18) based on a systematic procedure involving double coding. We used social network analysis to explore the relationships between users retweeting JUUL.One-quarter (25%) of users were determined to be adolescents. Social network analysis revealed a maximum path length of 4 and a positive assortativity (r = .29). There were 25 (9%) instances of an adolescent retweeting content from another adolescent, 35 (12%) of an adolescent retweeting from an adult, and 30 (11%) of an adult retweeting from an adolescent.JUUL's official Twitter account is being followed-and its messages are being shared-by adolescents. Rigorous policies and prevention programs are needed to curb adolescent exposure to JUUL content and discussions online.

There is growing interest in conducting public health research using data from social media. In particular, Twitter “infoveillance” has demonstrated utility across health contexts. However, rigorous and reproducible methodologies for using Twitter data in public health are not yet well articulated, particularly those related to content analysis, which is a highly popular approach. In 2014, we gathered an interdisciplinary team of health science researchers, computer scientists, and methodologists to begin implementing an open-source framework for real-time infoveillance of Twitter health messages (RITHM). Through this process, we documented common challenges and novel solutions to inform future work in real-time Twitter data collection and subsequent human coding. The RITHM framework allows researchers and practitioners to use well-planned and reproducible processes in retrieving, storing, filtering, subsampling, and formatting data for health topics of interest. Further considerations for human coding of Twitter data include coder selection and training, data representation, codebook development and refinement, and monitoring coding accuracy and productivity. We illustrate methodological considerations through practical examples from formative work related to hookah tobacco smoking, and we reference essential methods literature related to understanding and using Twitter data.

To examine the association between degree of real-life closeness of social media (SM) contacts and depressive symptoms.Students ages 18-30 (N = 1124) were recruited in August 2016.Participants completed an online survey assessing SM use and depression. We used multivariable logistic regression to assess associations between real-life closeness of SM contacts and depressive symptoms.After controlling for covariates, each 10% increase in the proportion of SM friends with whom participants had no face-to-face relationship was associated with a 9% increase in odds of depressive symptoms (AOR = 1.09; 95% CI = 1.05-1.13). However, each 10% increase in the proportion of SM friends with whom participants had a close face-to-face relationship was associated with a 7% decrease in depressive symptoms (AOR = 0.93; 95% CI = 0.89-0.97).Having no in-person relationship with SM contacts is associated with increased depressive symptoms; however, having close in-person relationships with SM contacts is associated with decreased depressive symptoms.

This study applied collective action theories and network theories to examine the information sharing patterns among Twitter users to obtain sociopolitical legitimacy of their collective goal. The role of Twitter in facilitating private–public boundary crossing was defined in relation to main challenges of collective action. The hypotheses and research question were examined using Twitter data collected from an online campaign, which was created to bring about the release of a detained Syrian activist. Network analysis results showed significant geographic homophily effect, that is, participants located in the same region tended to share information with each other. In addition, the results indicated that more influential Twitter users tended to connect with less influential users to help spread information on the movement. Further content analysis showed that to better mobilize potential collective action participants, Twitter users utilized strategies to draw attention from citizen news media organizations, nonprofit advocacy organizations, public figures, and corporations.

Background. The use of electronic cigarettes (e-cigarette) offers potential to facilitate cigarette smoking cessation, yet potentially increases risk of cigarette smoking initiation. This relationship has been primarily modeled in mathematical ways that often do not represent real-world complexities, which could inform decisions regarding local prevention programs or policies. Aims. To develop a model of cigarette and e-cigarette use that combines current research on tobacco use and incorporates real-world geographic and demographic data. Method. We used a platform for developing agent-based models with demographic information representative of the population in Pennsylvania. We developed three models of cigarette and e-cigarette use. The primary outcome for each was the total number of users for cigarette, e-cigarette, and total nicotine. The first model applied current cigarette and e-cigarette data, the second tested the effect of implementing a program of e-cigarette education and policies, and the third considered a social contagion factor, where local schools functioned as a transmission vector. Results. The baseline and social contagion models found an overall decline in cigarette use but an increase in e-cigarette and total nicotine use. The education/policies model had declines in all categories. Sensitivity analysis suggested the importance of nuanced e-cigarette/cigarette interactions when modeling tobacco use. Discussion. Public health campaigns that focus on reducing youth e-cigarette usage can have a large effect. Social contagion should be strongly considered when studying e-cigarette spread. Conclusion. Targeted public health campaigns focused on reducing school prevalence of e-cigarette use may be particularly valuable.

BACKGROUND Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. OBJECTIVE This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. METHODS We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. RESULTS LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. CONCLUSIONS We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Exposure to e-cigarette (e-cig) marketing through traditional and online media can increase the risk of e-cig use among adolescents. Though popular e-cig brand JUUL has scaled back its social media promotions, outside vendors and individual users create posts about JUUL unregulated. Instagram’s image-focus offers an opportunity to document promotions that may appeal to adolescents. To assess the visual representation of JUUL on social media, we systematically classified discussion topics and themes of posts to Instagram. Of 25,428 unique Instagram images using the hashtag #JUUL retrieved between June 18 and July 18, 2018, a random subsample of 3,000 images (approximately 12% of the full data set) was drawn for human coding and analyses. Coded categories included relevance to JUUL, type of image, image appeal, and user type. Based on the number of relevant images, percentages of image type and appeal for each user type were calculated. There were 583 images that were coded as relevant. The majority of image types (64%) were classified as product (n =; 374). The most frequently coded type of appeal was flavors, found in 54% of images with appeal (n =; 149), followed by cartoons, found in 21% (n =; 57). Vendors were responsible for posting most of the images (n =; 291), followed by e-cig enthusiasts (n =; 99). Continued surveillance of e-cig products, such as JUUL, remains necessary to provide a foundation for potential regulatory protections against marketing and promotions that may appeal to youth.

This study addresses ethical questions about conducting health science research using network data from social media platforms. We provide examples of ethically problematic areas related to participant consent, expectation of privacy, and social media networks. Further, to illustrate how researchers can maintain ethical integrity while leveraging social media networks, we describe a study that demonstrates the ability to use social media to identify individuals affected by cancer. We discuss best practices and ethical guidelines for studying social media network data, including data collection, analysis, and reporting.

Background Given the low retention and lack of persistent support by traditional tobacco cessation programs, evidence-based smartphone app-supported interventions can be an important tobacco control component. The objective of this systematic review was to identify and evaluate the types of studies that use smartphone apps for interventions in tobacco cessation. Methods We conducted a systematic review of PubMed (1946–2019), EMBASE (1974–2019), and PsycINFO (1806–2019) databases with keywords related to smartphone-supported tobacco cessation. Included articles were required to meet 3 baseline screening criteria: 1) be written in English, 2) include an abstract, and 3) be a full, peer-reviewed manuscript. The criteria for the second level of review were: 1) primary outcome of tobacco cessation, 2) intervention study, and 3) smartphone app as primary focus of study. Results Of 1973 eligible manuscripts, 18 met inclusion criteria. Most studies (n = 17) recruited adult participants (18 + years); one included teens (16 + years). Tobacco cessation was usually self-reported (n = 11), compared to biochemical verification (n = 3) or both (n = 4). There were 11 randomized controlled trials, 4 of which reported statistically significant results, and 7 single-arm trials that reported a mean abstinence rate of 33.9%. Discussion The majority of studies that use tobacco cessation apps as an intervention delivery modality are mostly at the pilot/feasibility stage. The growing field has resulted in studies that varied in methodologies, study design, and inclusion criteria. More consistency in intervention components and larger randomized controlled trials are needed for tobacco cessation smartphone apps.

E-cigarette use has been increasing among middle school students. Intervention programs to prevent e-cigarette initiation administered by authority figures are met with more resistance from youth compared to peer-led programs. Therefore, this study aimed to assess the feasibility, acceptability, and implementation process of using social network analysis (SNA) to identify student peer leaders in schools and train them to deliver e-cigarette prevention programming to their peers. Nine schools were recruited to participate in the study during the 2019–2020 school year. Schools were assigned to one of three conditions: (1) expert; (2) peer-random (selected peer-leaders would teach to random students); and (3) peer-fixed (selected peer-leaders would teach to assigned students based on nominations). Study participation varied by day due to school attendance, with 686 participants at baseline and 608 at posttest. Almost all students who did not complete the study resulted from the interruption of schools being closed due to COVID-19. Implementation issues fell into three categories: (1) scheduling, (2) day-of logistics, and (3) student group dynamics. Overall, the results showed positive satisfaction among teachers, who unanimously found the program appropriate for the grade-level and that peer-leaders worked well within their groups. Peer-led students—both random and assigned—reported having more fun and willing to tell friends to try the program compared to expert-led students. This study demonstrated the feasibility of implementing a peer-led e-cigarette prevention program for 6(th) grade students, using SNA to provide intervention rigidity and validity.