Classification of Twitter Users Who Tweet About E-Cigarettes

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

BACKGROUND

Prior studies have demonstrated that the e-cigarette market contains a large number of brands. Identifying these existing e-cigarette brands is a key element of market surveillance, which will further assist in policy making and compliance checks.

OBJECTIVE

To facilitate the surveillance of the diverse product landscape in the e-cigarette market, we constructed a semantic database of e-cigarette brands that have appeared in the US market as of 2020-2022.

METHODS

In order to build the brand database, we searched and compiled e-cigarette brands from a comprehensive list of retail channels and sources, including (1) e-liquid and disposable brands sold in web-based stores, (2) e-cigarette brands sold in brick-and-mortar stores and collected by the Nielsen Retail Scanner Data, (3) e-cigarette brands compiled by Wikipedia, (4) self-reported e-cigarette brands from the 2020 International Tobacco Control Four-Country Smoking and Vaping (ITC 4CV) US survey, and (5) e-cigarette brands on Twitter. We also estimated the top 5 e-cigarette brands by sales volume in brick-and-mortar stores, by the frequency and variety of offerings in web-based shops, and by the frequency of self-reported brands from the 2020 ITC 4CV US survey.

RESULTS

As of 2020-2022, a total of 912 e-cigarette brands have been sold by various retail channels. During 2020-2022, the top 5 brands are JUUL, vuse, njoy, blu, and logic in brick-and-mortar stores; blu, king, monster, twist, and air factory for e-liquids in web-based stores; hyde, pod mesh, suorin, vaporlax, and xtra for disposables sold in web-based stores; and smok, aspire, vaporesso, innokin, and eleaf based on self-reported survey data.

CONCLUSIONS

As the US Food and Drug Administration enforces the premarket tobacco market authorization, many e-cigarette brands may become illegal in the US market. In this context, how e-cigarette brands evolve and consolidate in different retail channels will be critical for understanding the regulatory impacts on product availability. Our semantic database of e-cigarette brands can serve as a useful tool to monitor product and marketplace development, conduct compliance checks, assess manufacturers' marketing behaviors, and identify regulatory impacts.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

Background

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting.

Results

When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account.

Conclusions

e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Twitter use by the dementia community during COVID-19: a user classification and social network analysis

Purpose

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

Design/methodology/approach

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Findings

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

Originality/value

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

BACKGROUND

Eating disorders affect an increasing number of people. Social networks provide information that can help.

OBJECTIVE

We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain.

METHODS

We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model.

RESULTS

A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%).

CONCLUSIONS

Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.

Using Machine Learning for Pharmacovigilance: A Systematic Review

Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores. Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance. Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

Exploring the Twitter activity around the eighth meeting of the Conference of the Parties to the WHO Framework Convention on Tobacco Control

BACKGROUND

Tobacco companies' intentions to influence the WHO Framework Convention on Tobacco Control (FCTC) via the Conference of Parties (COP; the official biannual meeting where Parties review the Convention) are well documented. We aimed to analyse Twitter data to gain insights into tobacco industry tactics, arguments and allies.

METHODS

We retrieved 9089 tweets that included #COP8FCTC between 1 and 9 October 2018. We categorised the tweets' content and sentiment through manual coding and machine learning. We used an investigative procedure using publicly available information to categorise the most active Twitter users and investigate tobacco industry links. Network analysis was used to visualise interactions and detect communities.

RESULTS

Most tweets were about next-generation products (NGPs) or 'harm reduction' (54%) and tended to argue in support of NGPs; around one-quarter were critical of tobacco control (24%). The largest proportion of most active tweeters were NGP advocates, and slightly over half of those had either links to the Philip Morris International (PMI) funded Foundation for a Smoke-Free World (FSFW) and/or to the International Network of Nicotine Consumer Organisations, a network to whom the FSFW granted US$100 300 in 2018. PMI was the most active transnational tobacco company during COP8.

CONCLUSIONS

The nature of the activity on Twitter around COP8, including a substantial online presence by PMI executives and NGP advocates with links to organisations funded directly and indirectly by PMI, is highly consistent with PMI's 2014 corporate affairs strategy, which described engaging tobacco harm reduction advocates to 'amplify and leverage the debate on harm reduction' around events such as the COP.

Characterizing alternative and emerging tobacco product transition of use behavior on Twitter

OBJECTIVE

The objective of this study was to develop an inductive coding approach specific to characterizing user-generated social media conversations about transition of use of different tobacco and alternative and emerging tobacco products (ATPs).

RESULTS

A total of 40,206 tweets were collected from the Twitter public API stream that were geocoded from 2018 to 2019. Using data mining approaches, these tweets were then filtered for keywords associated with tobacco and ATP use behavior. This resulted in a subset of 5718 tweets, with 657 manually annotated and identified as associated with user-generated conversations about tobacco and ATP use behavior. The 657 tweets were coded into 9 parent codes: inquiry, interaction, observation, opinion, promote, reply, share knowledge, use characteristics, and transition of use behavior. The highest number of observations occurred under transition of use (43.38%, n = 285), followed by current use (39.27%, n = 258), opinions about use (0.07%, n = 46), and product promotion (0.06%, n = 37). Other codes had less than ten tweets that discussed these themes. Results provide early insights into how social media users discuss topics related to transition of use and their experiences with different and emerging tobacco product use behavior.

Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data

BACKGROUND

Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic.

OBJECTIVE

In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data.

METHODS

The study dataset containing COVID-19-related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19-related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette-related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette-related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups.

RESULTS

The US COVID-19 dataset consisted of 4,500,248 COVID-19-related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19-related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government's responses to the COVID-19 pandemic.

CONCLUSIONS

Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

Utilizing deep learning and graph mining to identify drug use on Twitter data

Background

The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined.

Methods

Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets.

Results

To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC’s of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification.

Conclusion

Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

BACKGROUND

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

OBJECTIVE

The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

METHODS

We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.

RESULTS

The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.

CONCLUSIONS

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

Infodemiology and Infoveillance: Scoping Review

BACKGROUND

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

OBJECTIVE

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

RESULTS

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

CONCLUSIONS

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis

Background

Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information.

Objective

The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail.

Methods

A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets’ metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed.

Results

A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet’s author was tagged as favorited by the community.

Conclusions

The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida—a decorative element in Arabic writing used to justify the text within lines—were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

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.

New Tobacco and Tobacco-Related Products: Early Detection of Product Development, Marketing Strategies, and Consumer Interest

Background

A wide variety of new tobacco and tobacco-related products have emerged on the market in recent years.

Objective

To understand their potential implications for public health and to guide tobacco control efforts, we have used an infoveillance approach to identify new tobacco and tobacco-related products.

Methods

Our search for tobacco(-related) products consists of several tailored search profiles using combinations of keywords such as “e-cigarette” and “new” to extract information from almost 9000 preselected sources such as websites of online shops, tobacco manufacturers, and news sites.

Results

Developments in e-cigarette design characteristics show a trend toward customization by possibilities to adjust temperature and airflow, and by the large variety of flavors of e-liquids. Additionally, more e-cigarettes are equipped with personalized accessories, such as mobile phones, applications, and Bluetooth. Waterpipe products follow the trend toward electronic vaping. Various heat-not-burn products were reintroduced to the market.

Conclusions

Our search for tobacco(-related) products was specific and timely, though advances in product development require ongoing optimization of the search strategy. Our results show a trend toward products resembling tobacco cigarettes vaporizers that can be adapted to the consumers’ needs. Our search for tobacco(-related) products could aid in the assessment of the likelihood of new products to gain market share, as a possible health risk or as an indicator for the need on independent and reliable information of the product to the general public.

Real Time Assessment of Young Adults' Attitudes toward Tobacco Messages

Objectives

We used ecological momentary assessment (EMA) to examine young adults' attitudes towards pro-tobacco messages encountered in real time and their association with intentions to use tobacco.

Methods

Young adults (N = 92, ages 18-29) recorded sightings of marketing or social media related to tobacco in real time via mobile app for 28 days. Participants reported message characteristics, their attitudes towards the message, and intentions to use the depicted product for each submission. We used generalized linear mixed models to examine factors related to attitude towards message and intentions to use tobacco.

Results

Messages depicting e-cigarettes (p < .001) or hookah (p < .05) were associated with significantly more favorable attitudes compared with traditional cigarettes. Positive attitude towards the message was significantly associated with intention to use the depicted product (p < .001). Messages depicting e-cigarettes and hookah were significantly associated with higher intention to use. Message source was not significantly related to attitudes towards the message or product use intentions.

Conclusions

Marketing featuring e-cigarettes and hookah is an important target for future regulation. Given that pro-tobacco and e-cigarette messages are prevalent online, future research should consider the Internet and social media as important venues for counter-marketing and intervention efforts.