Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages.

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

"Better Than Any DARE Program": Qualitative Analysis of Adolescent Reactions to EVALI Television Storylines.

Introduction. Research suggests that awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) among adolescents is associated with increased harm perception of e-cigarettes. The depiction of EVALI on three primetime medical dramas offers an opportunity to examine the use of these storylines for tobacco prevention education. Methods. We conducted four focus groups with seventh- and eighth-grade students at an urban middle school. Participants viewed three clips of scenes followed by a facilitated discussion as to the influence of the clips on knowledge and perceptions of e-cigarettes and the use of clips for tobacco prevention education. Two research assistants double-coded notes from the focus groups using a qualitative content analysis approach. Results. Our final sample included 78 adolescents; we obtained self-reported demographic information for 75. The majority of participants were 13 to 14 years of age (82.7%) and identified as cisgender female (52.0%) and Black (52.0%). No participants had knowledge of EVALI prior to viewing the clips. Comments made both during and after watching suggest the clips may have reinforced knowledge and perceptions of harm; participants stated that the clips could be a useful intervention tool. Viewing the clips also generated unprompted discussion about flavored products, tobacco advertising, other television programs, and marijuana. Conclusions. Clips featuring the depiction of EVALI on medical dramas may be an effective tool for raising awareness of e-cigarette use-related harms. These results offer a promising first step for future collaborative research between public health, adolescents, and schools to develop tobacco prevention education utilizing these clips.

COVID-19 Vaccine Hesitancy, Acceptance, and Promotion Among Healthcare Workers: A Mixed-Methods Analysis.

Even with vaccine mandates, COVID-19 vaccine hesitancy remains a concern among healthcare workers, in part due to their role in promoting vaccination among patients and communities. To examine COVID-19 vaccine hesitancy, acceptance, and promotion among healthcare workers, we conducted a mixed-methods analysis of (1) survey responses about COVID-19 vaccination and (2) Twitter messages (i.e., tweets) relevant to COVID-19 vaccination and healthcare. A total of 540 hospital employees completed the survey. Those that completed less than 80% of the survey or did not endorse employment at the hospital were excluded, resulting in a total of 511 valid responses; 93.2% reported receiving at least one dose of a COVID-19 vaccine. Approximately 1/3 of vaccinated individuals indicated they posted about receiving the vaccine on social media. Simultaneously, we analyzed a sample of 3845 tweets; 2299 (60%) were relevant to COVID-19 vaccination and 1863 (81%) were coded as authored by an individual. Of tweets authored by an individual, 6% (n = 106) were authored by a healthcare provider/health sciences student. Among relevant tweets, the most frequent code across all sentiment categories was related to the pharmaceutical industry (n = 529 tweets, 28%; n = 33, 31% of tweets authored by healthcare workers). Triangulation of results found themes including vaccine access, trust, and vaccine safety or negative health impacts. Results suggest that promoting the sharing of COVID-19 vaccine personal narratives on social media, combined with interventions targeting specific reasons for COVID-19 vaccine hesitancy and emphasizing freedom from fear once vaccinated could be effective at reducing COVID-19 vaccine hesitancy among this population.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets.

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.

E-Cigarette-Related Nicotine Misinformation on Social Media.

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.

Re-evaluating standards of human subjects protection for sensitive health data in social media networks.

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.

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

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.

Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking.

BACKGROUND: Hookah tobacco smoking (HTS) is a particularly important issue for public health professionals to address owing to its prevalence and deleterious health effects. Social media sites can be a valuable tool for public health officials to conduct informational health campaigns. Current social media platforms provide researchers with opportunities to better identify and target specific audiences and even individuals. However, we are not aware of systematic research attempting to identify audiences with mixed or ambivalent views toward HTS. OBJECTIVE: The objective of this study was to (1) confirm previous research showing positively skewed HTS sentiment on Twitter using a larger dataset by leveraging machine learning techniques and (2) systematically identify individuals who exhibit mixed opinions about HTS via the Twitter platform and therefore represent key audiences for intervention. METHODS: We prospectively collected tweets related to HTS from January to June 2016. We double-coded sentiment for a subset of approximately 5000 randomly sampled tweets for sentiment toward HTS and used these data to train a machine learning classifier to assess the remaining approximately 556,000 HTS-related Twitter posts. Natural language processing software was used to extract linguistic features (ie, language-based covariates). The data were processed by machine learning tools and algorithms using R. Finally, we used the results to identify individuals who, because they had consistently posted both positive and negative content, might be ambivalent toward HTS and represent an ideal audience for intervention. RESULTS: There were 561,960 HTS-related tweets: 373,911 were classified as positive and 183,139 were classified as negative. A set of 12,861 users met a priori criteria indicating that they posted both positive and negative tweets about HTS. CONCLUSIONS: Sentiment analysis can allow researchers to identify audience segments on social media that demonstrate ambiguity toward key public health issues, such as HTS, and therefore represent ideal populations for intervention. Using large social media datasets can help public health officials to preemptively identify specific audience segments that would be most receptive to targeted campaigns.

Tobacco Use Behaviors, Attitudes, and Demographic Characteristics of Tobacco Opinion Leaders and Their Followers: Twitter Analysis.

BACKGROUND: Tobacco-related content on social media is generated and propagated by opinion leaders on the Web who disseminate messages to others in their network, including followers, who then continue to spread the information. Opinion leaders can exert powerful influences on their followers' knowledge, attitudes, and behaviors; yet, little is known about the demographic characteristics and tobacco use behavior of tobacco opinion leaders on the Web and their followers, compared with general Twitter users. OBJECTIVE: In this study, we hypothesized that opinion leaders use more tobacco products and have higher nicotine dependence than the other 2 groups (eg, followers and general Twitter users) and that followers-those who spread messages by opinion leaders-would more likely be in demographic groups that are vulnerable to tobacco marketing influence (eg, young adults and lower educational attainment). METHODS: We constructed the social networks of people who tweet about tobacco and categorized them using a combination of social network and Twitter metrics. To understand the characteristics of tobacco opinion leaders and their followers, we conducted a survey of tobacco opinion leaders, their followers, and general Twitter users. The sample included 347 opinion leaders, 567 followers, and 519 general users. The opinion leaders had a median of 1000 followers, whereas followers and general users had fewer than 600 followers. RESULTS: Opinion leaders were more likely than their followers to report past month use of tobacco products; followers, in turn, were more likely to report past month use of these products than general Twitter users. The followers appeared to be an especially vulnerable group; they tended to be younger (mean age 22.4 years) and have lower education compared with the opinion leaders and general users. CONCLUSIONS: Followers of Twitter tobacco opinion leaders are a vulnerable group that might benefit from antitobacco education to counter the protobacco communications they see on social media.

Toward Real-Time Infoveillance of Twitter Health Messages.

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.

Waterpipe Promotion and Use on Instagram: #Hookah.

INTRODUCTION: Waterpipe (hookah) use is becoming more prevalent in the United States and abroad with potential implications for public health. As waterpipe use rapidly grows in popularity, novel data streams are needed that can help capture and document the social and environmental context in which individuals use, and are marketed, this emerging tobacco product. This study characterized waterpipe-related posts on Instagram in order to inform regulatory and policy activities in the United States. METHODS: Data were collected from Instagram, an image-based social media site. Inclusion criteria for this study comprised an Instagram post with the hashtag "#hookah" that was accompanied by geo-location metadata demonstrating that the post was inside the contiguous United States. Rules were established for coding themes of images (n = 1705). RESULTS: Seven percent of images depicted a single person using a waterpipe and/or blowing smoke, 25% depicted two or more persons lounging and/or using a waterpipe, 6% depicted waterpipes, coals or flavored tobacco without people, 18% of images were promotional material for hookah lounges and restaurant/bars/nightclubs referencing hookah in the text or depicting a waterpipe, 25% were non-waterpipe-related promotional material, 1% were sexually explicit material, and 18% other. 31% of all images depicted or referenced alcohol. 30% of posts provided geo-location from a hookah lounge, 56% from a restaurant/bar/nightclub, and 14% from other types of locations. CONCLUSIONS: The cross promotion of waterpipe and alcohol use by hookah lounges, and restaurants/bars/nightclubs suggests that poly-substance use is regularly depicted, and promoted, in nightlife entertainment as well as normalized on Instagram in the United States. IMPLICATIONS: In the US Instagram posts with the hashtag #hookah regularly depicted waterpipe use in conjunction with alcohol use. Instagram's focus on images facilitates picture-based advertising where hookah lounges promote drink specials at the same time nightclubs promote waterpipe specials. Instagram users often document the variety of shapes, sizes, and styles, of waterpipes, suggesting users take pride in their purchases and like to show their customized, and often times elaborate, waterpipes to their followers. Instagram has been underutilized in understanding tobacco related-behaviors and identifying tobacco-related promotional material.

Campaigns and counter campaigns: reactions on Twitter to e-cigarette education.

BACKGROUND: Social media present opportunities for public health departments to galvanise interest in health issues. A challenge is creating content that will resonate with target audiences, and determining reactions to educational material. Twitter can be used as a real-time surveillance system to capture individuals' immediate reactions to education campaigns and such information could lead to better campaigns in the future. A case study testing Twitter's potential presented itself when the California Department of Public Health launched its 'Still Blowing Smoke' media campaign about the potential harmful effects of e-cigarettes. Pro-e-cigarette advocacy groups, in response, launched a counter campaign titled 'Not Blowing Smoke'. This study tracked the popularity of the two campaigns on Twitter, analysed the content of the messages and determined who was involved in these discussions. METHODS: The study period was from 22 March 2015 to 27 June 2015. A stratified sampling procedure supplied 2192 tweets for analysis. Content analysis identified pro, anti and neutral e-cigarette tweets, and five additional themes: Marketing Elements, Money, Regulation/propaganda, Health, and Other. Metadata were analysed to obtain additional information about Twitter accounts. RESULTS: 'Not Blowing Smoke' was referenced more frequently than 'Still Blowing Smoke' on Twitter. Messages commonly objected to government regulation of e-cigarettes, refuted claims that e-cigarette manufactures were aligned with big tobacco, and touted the health benefits of e-cigarette use. E-cigarette companies and vape shops used campaign slogans to communicate with customers on Twitter. CONCLUSIONS: Findings showed the time dynamics of Twitter and the possibility for real-time monitoring of education campaigns.

Images of Little Cigars and Cigarillos on Instagram Identified by the Hashtag #swisher: Thematic Analysis.

BACKGROUND: Little cigar and cigarillo use is becoming more prevalent in the United States and elsewhere, with implications for public health. As little cigar and cigarillo use grows in popularity, big social media data (eg, Instagram, Google Web Search, Twitter) can be used to capture and document the context in which individuals use, and are marketed, these tobacco products. Big social media data may allow people to organically demonstrate how and why they use little cigars and cigarillos, unprimed by a researcher, without instrument bias and at low costs. OBJECTIVE: This study characterized Swisher (the most popular brand of cigars in the United States, controlling over 75% of the market share) little cigar- and cigarillo-related posts on Instagram to inform the design of tobacco education campaigns and the development of future tobacco control efforts, and to demonstrate the utility in using big social media data in understanding health behaviors. METHODS: We collected images from Instagram, an image-based social media app allowing users to capture, customize, and post photos on the Internet with over 400 million active users. Inclusion criteria for this study consisted of an Instagram post with the hashtag "#swisher". We established rules for coding themes of images. RESULTS: Of 1967 images collected, 486 (24.71%) were marijuana related, 348 (17.69%) were of tobacco products or promotional material, 324 (16.47%) showed individuals smoking, 225 (11.44%) were memes, and 584 (29.69%) were classified as other (eg, selfies, food, sexually explicit images). Of the marijuana-related images, 157/486 (32.3%) contained a Swisher wrapper, indicating that a Swisher product was used in blunt making, which involves hollowing out a cigar and refilling it with marijuana. CONCLUSIONS: Images from Instagram may be used to complement and extend the study of health behaviors including tobacco use. Images may be as valuable as, or more valuable than, words from other social media platforms alone. Posts on Instagram showing Swisher products, including blunt making, could add to the normalization of little cigar and cigarillo use and is an area of future research. Tobacco control researchers should design social media campaigns to combat smoking imagery found on popular sites such as Instagram.

Methods for Coding Tobacco-Related Twitter Data: A Systematic Review.

BACKGROUND: As Twitter has grown in popularity to 313 million monthly active users, researchers have increasingly been using it as a data source for tobacco-related research. OBJECTIVE: The objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies. METHODS: Data sources included PsycINFO, Web of Science, PubMed, ABI/INFORM, Communication Source, and Tobacco Regulatory Science. Searches were limited to peer-reviewed journals and conference proceedings in English from January 2006 to July 2016. The initial search identified 274 articles using a Twitter keyword and a tobacco keyword. One coder reviewed all abstracts and identified 27 articles that met the following inclusion criteria: (1) original research, (2) focused on tobacco or a tobacco product, (3) analyzed Twitter data, and (4) coded Twitter data categorically. One coder extracted data collection and coding methods. RESULTS: E-cigarettes were the most common type of Twitter data analyzed, followed by specific tobacco campaigns. The most prevalent data sources were Gnip and Twitter's Streaming application programming interface (API). The primary methods of coding were hand-coding and machine learning. The studies predominantly coded for relevance, sentiment, theme, user or account, and location of user. CONCLUSIONS: Standards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter's databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations.

Vaping on Instagram: cloud chasing, hand checks and product placement.

INTRODUCTION: This study documented images posted on Instagram of electronic cigarettes (e-cigarette) and vaping (activity associated with e-cigarette use). Although e-cigarettes have been studied on Twitter, few studies have focused on Instagram, despite having 500 million users. Instagram's emphasis on images warranted investigation of e-cigarettes, as past tobacco industry strategies demonstrated that images could be used to mislead in advertisements, or normalise tobacco-related behaviours. Findings should prove informative to tobacco control policies in the future. METHODS: 3 months of publicly available data were collected from Instagram, including images and associated metadata (n=2208). Themes of images were classified as (1) activity, for example, a person blowing vapour; (2) product, for example, a personal photo of an e-cigarette device; (3) advertisement; (4) text, for example, 'meme' or image containing mostly text and (5) other. User endorsement (likes) of each type of image was recorded. Caption text was analysed to explore different trends in vaping and e-cigarette-related text. RESULTS: Analyses found that advertisement-themed images were most common (29%), followed by product (28%), and activity (18%). Likes were more likely to accompany activity and product-themed images compared with advertisement or text-themed images (p<0.01). Vaping-related text greatly outnumbered e-cigarette-related text in the image captions. CONCLUSIONS: Instagram affords its users the ability to post images of e-cigarette-related behaviours and gives advertisers the opportunity to display their product. Future research should incorporate novel data streams to improve public health surveillance, survey development and educational campaigns.

Characterizing online social support for alcohol use disorder: A mixed-methods approach.

BACKGROUND: Online social media communities are increasingly popular venues for discussing alcohol use disorder (AUD) and recovery. Little is known about distinct contexts of social support that are exchanged in this milieu, which are critical to understanding the social dynamics of online recovery support. METHODS: We randomly selected one post per day over the span of a year from the StopDrinking recovery forum. Direct responses to posts were double coded within an established theoretical framework of social support. Within a mixed-methods research framework, we quantified the linguistic characteristics of 1386 responses (i.e., text length, complexity, and sentiment) and qualitatively explored themes within and among different types of social support. RESULTS: Emotional support was most prevalent (74% of responses) and appeared as the sole form of support in 38% of responses. Emotionally supportive responses were significantly shorter, less complex, and more positively valenced than other support types. Appraisal support was also common in 55% of responses, while informational support was identified in only 17%. There was substantial overlap among support types, with 40% of responses including two or more types. Salient themes included the common use of community-specific acronyms in emotional support. Appraisal support conveyed feedback about attitudes and behaviors that are perceived as (un-) favorable for AUD recovery. Informational support responses were composed primarily of recommendations for self-help literature, clinical treatment approaches, and peer recovery programs. CONCLUSIONS: Social support in this sample was primarily emotional in nature, with other types of support included to provide feedback and guidance (i.e., appraisal support) and supplemental recovery resources (i.e., informational support). The provided social support framework can be helpful in characterizing community dynamics among heterogeneous online AUD recovery support forums. This framework could also be helpful in considering changes in support approaches that correspond to progress in recovery.

Characterizing the Influence of Television Health Entertainment Narratives in Lay Populations: A Scoping Review.

OBJECTIVE: To conduct a scoping review of published literature examining the influence of health storylines from fictional television programs on viewers. DATA SOURCE: We performed literature searches in Medline, PsycINFO, and Mass Media Complete in October 2021, and examined bibliographies of included articles and conducted forward searching using Web of Science with included articles. STUDY INCLUSION AND EXCLUSION CRITERIA: Selected studies were required to be original research published in English, involve exposure to fictional television programming by individuals not in the medical field, and assess associations between exposure and health-related outcomes. DATA EXTRACTION: Article screening and data abstraction were performed by two independent researchers using DistillerSR (Cohen's κ range: .73-1.00). DATA SYNTHESIS: We analyzed and qualitatively described the data using methods of scoping reviews described by PRISMA-ScR. RESULTS: Of 5,537 unique records identified, 165 met inclusion criteria. The most frequently studied program was ER (n = 22, 13.3%). Most studies had adult participants (n = 116, 70.3%) and used quantitative methods (n = 136, 82.4%). The most frequently examined health topics were sexual behavior (n = 28, 17.0%) and mental health (n = 28, 17.0%). Exposure had a positive influence on viewers' health-related outcomes in 28.5% (n = 47) of studies. CONCLUSION: Health storylines on fictional television influence viewers. Future research could address gaps identified in this review to further elucidate the influence of this programming on health promotion and disease prevention.

Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content.

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.

Electronic Vapor Product Use and Violence Victimization Among a Nationally Representative Sample of Adolescents.

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.