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

Introduction: This study analyzes age-differentiated Reddit conversations about ENDS. Methods: This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded. Results: Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior. Conclusions: Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.

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

BACKGROUND: Scientists identified vitamin E acetate (VEA) and "Dank Vapes" (a fake brand of tetrahydrocannabinol [THC] vaping products) as contributors to the 2019-2020 outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). On social media, people who post about vaping or THC discussed the causes of EVALI. We examined whether Reddit conversations may have served as early signals of the outbreak. METHODS: We collected Reddit posts from March 2018 to February 2020 on vaping- and THC-related subreddits that mentioned VEA or Dank Vapes. We identified peaks in post volume, examined post content, and used natural language processing to identify terms most characteristic of posts. RESULTS: There were almost no posts about VEA before EVALI. Subsequently, there were two peaks, both referencing media coverage of scientific findings that linked VEA to EVALI. Discussion regularly referenced concerns about the legitimacy of Dank Vapes before EVALI; peaks in posts were largely unrelated to scientific findings or media coverage of those findings. The terms most characteristic of VEA posts were EVALI-related; those most characteristic of Dank Vapes posts were about quality or legitimacy. CONCLUSIONS: Although posts about VEA and Dank Vapes did not predict the outbreak, the public health community could use social media to encourage people who vape or use THC to report future health concerns (e.g., through FDA's Safety Reporting Portal). Researchers and regulators could also use social media to see if potentially problematic products, such as Dank Vapes, have a history of concern among individuals who use those products.

An Experimental Study of Nicotine Warning Statements in E-cigarette Tweets.

INTRODUCTION: It is unclear whether warnings on electronic cigarette (e-cigarette) advertisements required by the US Food and Drug Administration (FDA) will apply to social media. Given the key role of social media in marketing e-cigarettes, we seek to inform FDA decision making by exploring how warnings on various tweet content influence perceived healthiness, nicotine harm, likelihood to try e-cigarettes, and warning recall. METHODS: In this 2 × 4 between-subjects experiment participants viewed a tweet from a fictitious e-cigarette brand. Four tweet content versions (e-cigarette product, e-cigarette use, e-cigarette in social context, unrelated content) were crossed with two warning versions (absent, present). Adult e-cigarette users (N = 994) were recruited via social media ads to complete a survey and randomized to view one of eight tweets. Multivariable regressions explored effects of tweet content and warning on perceived healthiness, perceived harm, and likelihood to try e-cigarettes, and tweet content on warning recall. Covariates were tobacco and social media use and demographics. RESULTS: Tweets with warnings elicited more negative health perceptions of the e-cigarette brand than tweets without warnings (p < .05). Tweets featuring e-cigarette products (p < .05) or use (p < .001) elicited higher warning recall than tweets featuring unrelated content. CONCLUSIONS: This is the first study to examine warning effects on perceptions of e-cigarette social media marketing. Warnings led to more negative e-cigarette health perceptions, but no effect on perceived nicotine harm or likelihood to try e-cigarettes. There were differences in warning recall by tweet content. Research should explore how varying warning content (text, size, placement) on tweets from e-cigarette brands influences health risk perceptions. IMPLICATIONS: FDA's 2016 ruling requires warnings on advertisements for nicotine-containing e-cigarettes, but does not specify whether this applies to social media. This study is the first to examine how e-cigarette warnings in tweets influence perceived healthiness and harm of e-cigarettes, which is important because e-cigarette brands are voluntarily including warnings on Twitter and Instagram. Warnings influenced perceived healthiness of the e-cigarette brand, but not perceived nicotine harm or likelihood to try e-cigarettes. We also saw higher recall of warning statements for tweets featuring e-cigarettes. Findings suggest that expanding warning requirements to e-cigarette social media marketing warrants further exploration and FDA consideration.

Effect of menthol cigarette and other menthol tobacco product bans on tobacco purchases in the RTI iShoppe virtual convenience store.

OBJECTIVE: To test how a potential US ban of menthol products or replacement with 'green' products and ads could influence tobacco purchases. METHODS: US adult menthol smokers (N=1197) were recruited via an online panel and randomly assigned to complete a shopping task in one of four versions (experimental conditions) of the RTI iShoppe virtual store: (1) no ban, (2) replacement of menthol cigarettes and ads with green replacement versions, (3) menthol cigarette ban and (4) all menthol tobacco product ban. Logistic regressions assessed the effect of condition on tobacco purchases. RESULTS: Participants in the menthol cigarette ban (OR=0.67, 95% CI 0.48 to 0.92) and all menthol product ban conditions (OR=0.60, 95% CI 0.43 to 0.83) were less likely to purchase cigarettes of any type than participants in the no ban condition. Participants in the green replacement (OR=1.74, 95% CI 1.13 to 2.70), menthol cigarette ban (OR=3.40, 95% CI 2.14 to 5.41) and all menthol product ban conditions (OR=3.14, 95% CI 1.97 to 5.01) were more likely to purchase a cigarette brand different from their usual brand than participants in the no ban condition. CONCLUSIONS: Our findings suggest that menthol bans could have great public health impact by reducing cigarette purchases. However, tobacco marketing strategies, such as creating green (or other replacement) versions of menthol cigarettes, may undermine public health benefits of a menthol ban by prompting purchases of non-menthol cigarettes. Our findings highlight the importance of taking tobacco marketing tactics into consideration in tobacco product regulation.

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.

Social media data are increasingly used by researchers to gain insights on individuals' behaviors and opinions. Platforms like Twitter provide access to individuals' postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents' Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user's friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents' tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.

Deception and Shopping Behavior Among Current Cigarette Smokers: A Web-Based, Randomized Virtual Shopping Experiment.

BACKGROUND: Virtual stores can be used to identify influences on consumer shopping behavior. Deception is one technique that may be used to attempt to increase the realism of virtual stores. OBJECTIVE: The objective of the experiment was to test whether the purchasing behavior of participants in a virtual shopping task varied based on whether they were told that they would receive the products they selected in a virtual convenience store (a form of deception) or not. METHODS: We recruited a US national sample of 402 adult current smokers by email from an online panel of survey participants. They completed a fully automated randomized virtual shopping experiment with a US $15 or US $20 budget in a Web-based virtual convenience store. We told a random half of participants that they would receive the products they chose in the virtual store or the cash equivalent (intervention condition), and the other random half simply to conduct a shopping task (control condition). We tested for differences in demographics, tobacco use behaviors, and in-store purchases (outcome variable, assessed by questionnaire) by experimental condition. RESULTS: The characteristics of the participants (398/402, 99.0% with complete data) were comparable across conditions except that the intervention group contained slightly more female participants (103/197, 52.3%) than the control group (84/201, 41.8%; P=.04). We did not find any other significant differences in any other demographic variables or tobacco use, or in virtual store shopping behaviors, including purchasing any tobacco (P=.44); purchasing cigarettes (P=.16), e-cigarettes (P=.54), cigars (P=.98), or smokeless tobacco (P=.72); amount spent overall (P=.63) or on tobacco (P=.66); percentage of budget spent overall (P=.84) or on tobacco (P=.74); number of total items (P=.64) and tobacco items purchased (P=.54); or total time spent in the store (P=.07). CONCLUSIONS: We found that telling participants that they will receive the products they select in a virtual store did not influence their purchases. This finding suggests that deception may not affect consumer behavior and, as a result, may not be necessary in virtual shopping experiments.

Predicting age groups of Twitter users based on language and metadata features.

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

Risk Factors for Youth E-Cigarette "Vape Trick" Behavior.

PURPOSE: Some adolescent users of e-cigarettes and other electronic vaping products (EVPs) report performing "vape tricks" (exhaling aerosol to make shapes). However, little is known about this behavior. We examined the frequency of performing and watching vape tricks and the characteristics of those most likely to perform vape tricks among a sample of adolescent EVP users. METHODS: We used social media ads to recruit a national convenience sample of U.S. adolescents (n = 1,729) to participate in an online survey in September 2016. Inclusion criteria required participants to be aged 15-17 years and to have used EVPs at least once in the past 30 days. RESULTS: The majority of EVP-using adolescents reported trying (77.8%) and watching vape tricks in person (83.7%) or online (74.0%). Risk factors for performing tricks included using advanced vaping devices, vaping every day, white race, moderate levels of seeing and sharing vaping information on social media, and believing that EVP use is more normative among peers. Likelihood of trying vape tricks decreased as beliefs about the harmfulness of EVPs increased. CONCLUSIONS: Vape tricks pose a potential threat to adolescent health if they encourage nonusers to initiate or current EVP users to use more frequently or switch to advanced devices that produce more harmful chemical emissions. Further research should examine the possible health effects of performing vape tricks, and future public health campaigns should be informed by an understanding of the appeal of this activity for adolescents.

Quantifying how smokers value attributes of electronic cigarettes.

INTRODUCTION: Rates of electronic cigarette (e-cigarette) use have increased quickly among US adults (3.3% in 2010 to 8.5% in 2013) and youth (4.5% in 2013 to 13.4% in 2014). As state and local governments consider regulatory policies, understanding what smokers believe about e-cigarettes and how they value e-cigarettes is important. METHODS: Using data from a convenience sample of Florida adult smokers (N=765), we investigated the value smokers place on specific attributes of e-cigarettes (availability of flavours, effectiveness of e-cigarettes as a cessation aid, healthier alternative to regular cigarettes, ability to use e-cigarettes in public places) by asking smokers how much they would be willing to pay for e-cigarettes with and without each of these attributes. RESULTS: For cigarette-only and dual users, losing the ability to use an e-cigarette as a quit aid and losing the harm reduction of an e-cigarette significantly reduced the price respondents were willing to pay for an e-cigarette. For cigarette-only users, not being able to use an e-cigarette indoors and losing flavours also significantly reduced the price respondents were willing to pay for an e-cigarette. CONCLUSION: Our results suggest that smokers value multiple attributes of e-cigarettes. Our valuation measures also appear to align with smokers' beliefs about e-cigarettes.

Identifying e-cigarette vape stores: description of an online search methodology.

BACKGROUND: Although the overall impact of Electronic Nicotine Delivery Systems (ENDS) on public health is unclear, awareness, use, and marketing of the products have increased markedly in recent years. Identifying the increasing number of 'vape stores' that specialise in selling ENDS can be challenging given the lack of regulatory policies and licensing. This study assesses the utility of online search methods in identifying ENDS vape stores. METHODS: We conducted online searches in Google Maps, Yelp, and YellowPages to identify listings of ENDS vape stores in Florida, and used a crowdsourcing platform to call and verify stores that primarily sold ENDS to consumers. We compared store listings generated from the online search and crowdsourcing methodology to list licensed tobacco and ENDS retailers from the Florida Department of Business and Professional Regulation. RESULTS: The combined results from all three online sources yielded a total of 403 ENDS vape stores. Nearly 32.5% of these stores were on the state tobacco licensure list, while 67.5% were not. Accuracy of online results was highest for Yelp (77.6%), followed by YellowPages (77.1%) and Google (53.0%). CONCLUSIONS: Using the online search methodology we identified more ENDS vape stores than were on the state tobacco licensure list. This approach may be a promising strategy to identify and track the growth of ENDS vape stores over time, especially in states without a systematic licensing requirement for such stores.

Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study.

BACKGROUND: Marketing and use of electronic cigarettes (e-cigarettes) and other electronic nicotine delivery devices have increased exponentially in recent years fueled, in part, by marketing and word-of-mouth communications via social media platforms, such as Twitter. OBJECTIVE: This study examines Twitter posts about e-cigarettes between 2008 and 2013 to gain insights into (1) marketing trends for selling and promoting e-cigarettes and (2) locations where people use e-cigarettes. METHODS: We used keywords to gather tweets about e-cigarettes between July 1, 2008 and February 28, 2013. A randomly selected subset of tweets was manually coded as advertising (eg, marketing, advertising, sales, promotion) or nonadvertising (eg, individual users, consumers), and classification algorithms were trained to code the remaining data into these 2 categories. A combination of manual coding and natural language processing methods was used to indicate locations where people used e-cigarettes. Additional metadata were used to generate insights about users who tweeted most frequently about e-cigarettes. RESULTS: We identified approximately 1.7 million tweets about e-cigarettes between 2008 and 2013, with the majority of these tweets being advertising (93.43%, 1,559,508/1,669,123). Tweets about e-cigarettes increased more than tenfold between 2009 and 2010, suggesting a rapid increase in the popularity of e-cigarettes and marketing efforts. The Twitter handles tweeting most frequently about e-cigarettes were a mixture of e-cigarette brands, affiliate marketers, and resellers of e-cigarette products. Of the 471 e-cigarette tweets mentioning a specific place, most mentioned e-cigarette use in class (39.1%, 184/471) followed by home/room/bed (12.5%, 59/471), school (12.1%, 57/471), in public (8.7%, 41/471), the bathroom (5.7%, 27/471), and at work (4.5%, 21/471). CONCLUSIONS: Twitter is being used to promote e-cigarettes by different types of entities and the online marketplace is more diverse than offline product offerings and advertising strategies. E-cigarettes are also being used in public places, such as schools, underscoring the need for education and enforcement of policies banning e-cigarette use in public places. Twitter data can provide new insights on e-cigarettes to help inform future research, regulations, surveillance, and enforcement efforts.

A Randomized Trial of the Effect of E-cigarette TV Advertisements on Intentions to Use E-cigarettes.

INTRODUCTION: Adolescents' use of electronic cigarettes (e-cigarettes) and exposure to e-cigarette TV advertising have increased in recent years, despite questions about their safety. The current study tests whether exposure to e-cigarette TV advertisements influences intentions to use e-cigarettes in the future and related attitudes. METHODS: A parallel-group randomized controlled experiment was conducted and analyzed in 2014 using an online survey with a convenience sample of 3,655 U.S. adolescents aged 13-17 years who had never tried e-cigarettes. Adolescents in the treatment group viewed four e-cigarette TV advertisements. RESULTS: Adolescents in the treatment group reported a greater likelihood of future e-cigarette use compared with the control group. ORs for the treatment group were 1.54 (p=0.001) for trying an e-cigarette soon; 1.43 (p=0.003) for trying an e-cigarette within the next year; and 1.29 (p=0.02) for trying an e-cigarette if a best friend offered one. Adolescents in the treatment group had higher odds of agreeing that e-cigarettes can be used in places where cigarettes are not allowed (OR=1.71, p<0.001); can be used without affecting those around you (OR=1.83, p<0.001); are a safer alternative to cigarettes (OR=1.19, p=0.01); and are less toxic (OR=1.16, p=0.03). CONCLUSIONS: Exposure to e-cigarette advertising had relatively large and consistent effects across experimental outcomes. Together with the simultaneous increase in e-cigarette advertising exposure and e-cigarette use among adolescents, findings suggest that e-cigarette advertising is persuading adolescents to try this novel product. This raises concerns that continued unregulated e-cigarette advertising will contribute to potential individual- and population-level harm.

Youth tobacco product use in the United States.

BACKGROUND: Noncigarette tobacco products are increasingly popular among youth, especially cigarette smokers. Understanding multiple tobacco product use is necessary to assess the effects of tobacco products on population health. This study examines multiple tobacco product use and associated risk factors among US youth. METHODS: Estimates of current use were calculated for cigarettes, cigars, smokeless tobacco, hookah, e-cigarettes, pipes, bidis, kreteks, snus, and dissolvable tobacco by using data from the 2012 National Youth Tobacco Survey (n = 24 658), a nationally representative sample of US middle and high school students. Associations between use patterns and demographic characteristics were examined by using multinomial logistic regression. RESULTS: Among youth, 14.7% currently use 1 or more tobacco products. Of these, 2.8% use cigarettes exclusively, and 4% use 1 noncigarette product exclusively; 2.7% use cigarettes with another product (dual use), and 4.3% use 3 or more products (polytobacco use). Twice as many youth use e-cigarettes alone than dual use with cigarettes. Among smokers, polytobacco use was significantly associated with male gender (adjusted relative risk ratio [aRRR] = 3.71), by using flavored products (aRRR = 6.09), nicotine dependence (aRRR = 1.91), tobacco marketing receptivity (aRRR = 2.52), and perceived prevalence of peer use of tobacco products (aRRR = 3.61, 5.73). CONCLUSIONS: More than twice as many youth in the United States currently use 2 or more tobacco products than cigarettes alone. Continued monitoring of tobacco use patterns is warranted, especially for e-cigarettes. Youth rates of multiple product use involving combustible products underscore needs for research assessing potential harms associated with these patterns.

Using mass media campaigns to reduce youth tobacco use: a review.

OBJECTIVE: This review synthesizes the published literature on using mass media campaigns to reduce youth tobacco use, with particular focus on effects within population subgroups and the relative effectiveness of campaign characteristics. DATA SOURCE: A search of PubMed and PsycINFO conducted in March of 2014 yielded 397 studies with 34 suitable for inclusion. STUDY INCLUSION AND EXCLUSION CRITERIA: Included were quantitative studies that evaluate an antitobacco media campaign intended to influence youth cognitions or behavior or explore the relative effectiveness of campaign characteristics among youth. DATA EXTRACTION: An automated search and assessment of suitability for inclusion was done. DATA SYNTHESIS: Study outcomes were compared and synthesized. RESULTS: Antitobacco media campaigns can be effective across racial/ethnic populations, although the size of the campaign effect may differ by race/ethnicity. Evidence is insufficient to determine whether campaign outcomes differ by socioeconomic status (SES) and population density. Youth are more likely to recall and think about advertising that includes personal testimonials; a surprising narrative; and intense images, sound, and editing. Evidence in support of using a health consequences message theme is mixed; an industry manipulation theme may be effective in combination with a health consequences message. Research is insufficient to determine whether advertising with a secondhand smoke or social norms theme influences youth tobacco use. CONCLUSION: Our recommendation is to develop antitobacco campaigns designed to reach all at-risk youth, which can be effective across racial/ethnic populations. Research priorities include assessing campaign influence among lower SES and rural youth, disentangling the effects of message characteristics, and assessing the degree to which this body of evidence may have changed as a result of changes in youth culture and communication technology.

Crowdsourcing data collection of the retail tobacco environment: case study comparing data from crowdsourced workers to trained data collectors.

OBJECTIVE: To assess whether crowdsourcing is a viable option for conducting surveillance of point of sale (POS) tobacco marketing practices. METHODS: We posted jobs to an online crowdsourcing platform to audit 194 Florida licensed tobacco retailers over a 3-week period. During the same period, trained data collectors conducted audits at the same retail locations. Data were collected on cigarette advertising, cigarette promotions and product availability (electronic cigarettes, snus and dissolvables). We compared data collected by crowdsourced workers and trained staff and computed frequencies, percent agreement and inter-rater reliability. Photographs of e-cigarettes and exterior cigarette advertisements submitted by crowdsourced workers were used to validate responses. RESULTS: Inter-rater reliability between crowdsourced and trained data collectors was moderate to high for coding exterior cigarette advertisements, product availability and some tobacco promotions, but poor to fair when coding presence of sales and interior cigarette advertisements. Photos submitted by crowdsourced workers confirmed e-cigarette availability that was missed by trained data collectors in three stores. CONCLUSIONS: Crowdsourcing may be a promising form of data collection for some POS tobacco measures. Future studies should examine the cost-effectiveness of crowdsourcing compared with traditional trained data collectors and assess which POS measures are most amenable to crowdsourcing.

Adult smokers' receptivity to a television advert for electronic nicotine delivery systems.

OBJECTIVE: The aim of the present work was to examine adult smokers' awareness of and receptivity to an electronic nicotine delivery system (ENDS) television advert, and whether viewing the advert influenced urge to smoke and intention to try ENDS. METHODS: A television advert for ENDS brand blu eCigs was shown to an online convenience sample of 519 Florida adult smokers. We measured current smokers' awareness of and receptivity to the advert, and whether seeing the advert influenced their thoughts about smoking or quitting, urge to smoke and intention to try ENDS. Results were stratified by prior ENDS use. RESULTS: Approximately 62.3% of current smokers were aware of the advert. Smokers found the advert informative (73.8%), attention grabbing (67.5%) and innovative (64.5%), with prior ENDS users rating the advert more favourably than non-users. Seeing the advert elicited an urge to smoke (mean 42.1, SD=1.9) and thoughts about smoking cigarettes (75.8%) as well as quitting (74.6%). Prior END users were significantly more likely than non-users to report thinking about smoking cigarettes after seeing the advert (P<0.05). Most smokers said ENDS were 'made for people like them' (88.6%) and they would try ENDS in the future (66.0%). CONCLUSIONS: Smokers are receptive to ENDS television adverts and report intention to try ENDS after viewing the advert. Future studies should monitor ENDS advertising and examine how exposure to ENDS adverts influences smokers' use of ENDS, dual use with cigarettes and cessation behaviour.

The use of social media by state tobacco control programs to promote smoking cessation: a cross-sectional study.

BACKGROUND: The promotion of evidence-based cessation services through social media sites may increase their utilization by smokers. Data on social media adoption and use within tobacco control programs (TCPs) have not been reported. OBJECTIVE: This study examines TCP use of and activity levels on social media, the reach of TCP sites, and the level of engagement with the content on sites. METHODS: A cross-sectional descriptive study of state TCP social media sites and their content was conducted. RESULTS: In 2013, 60% (30/50) of TCPs were using social media. Approximately one-quarter (26%, 13/50) of all TCPs used 3 or more social media sites, 24% (12/50) used 2, and 10% (5/50) used 1 site. Overall, 60% (30/50) had a Facebook page, 36% (18/50) had a Twitter page, and 40% (20/50) had a YouTube channel. The reach of social media was different across each site and varied widely by state. Among TCPs with a Facebook page, 73% (22/30) had less than 100 likes per 100,000 adults in the state, and 13% (4/30) had more than 400 likes per 100,000 adults. Among TCPs with a Twitter page, 61% (11/18) had less than 10 followers per 100,000 adults, and just 1 state had more than 100 followers per 100,000 adults. Seven states (23%, 7/30) updated their social media sites daily. The most frequent social media activities focused on the dissemination of information rather than interaction with site users. Social media resources from a national cessation media campaign were promoted infrequently. CONCLUSIONS: The current reach of state TCP social media sites is low and most TCPs are not promoting existing cessation services or capitalizing on social media's interactive potential. TCPs should create an online environment that increases participation and 2-way communication with smokers to promote free cessation services.