Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter

Adolescents and Young Adults Use of Social Media and Following of e-Cigarette Influencers

BACKGROUND

With high rates of both e-cigarette and social media use among adolescents and young adults (AYAs), social media influencers who promote e-cigarettes are particularly concerning but are understudied. We examined the association between AYAs' use of 11 different social media platforms (e.g. Facebook, Twitter, Instagram, TikTok, and YouTube) and exposure to social media e-cigarette influencers.

OBJECTIVES

From November 2022 to February 2023, we conducted an online, US national survey of AYAs (14-29 years) who endorsed past-30-day e-cigarette use. We used binomial logistic regression to examine associations between the frequency of use of each social media platform and following e-cigarette influencers, controlling for age, sex, race, ethnicity, e-cigarette use frequency, and other tobacco and substance use (i.e., alcohol and cannabis). The model was stratified by adolescents (14-17 years; n = 293) and young adults (18-29 years; n = 654).

RESULTS

The most frequently used social media platforms were Snapchat, TikTok and Instagram among adolescents, and YouTube, Instagram and TikTok among young adults. In adjusted models, following e-cigarette influencers was associated with more frequent use of TikTok (adjusted odds ratio [95% CI]; 1.33 [1.05, 1.68]) and Pinterest (1.18 [1.02, 1.38]) among adolescents, and more frequent use of Twitter (1.17 [1.06, 1.29]) among young adults.

CONCLUSIONS

The use of different platforms was associated with exposure to e-cigarette influencers: TikTok and Pinterest among adolescents and Twitter among young adults. These findings can inform tobacco regulatory policy and social media platform restrictions of e-cigarette influencers on the platforms that are popular among AYAs.

An Analysis of Arguments Advanced via Twitter in an Advocacy Campaign to Promote Electronic Nicotine Delivery Systems

INTRODUCTION

Advocates of electronic nicotine delivery systems (ENDS) increasingly use Twitter to promote liberal ENDS policies. "World Vape Day" (WVD) is an annual campaign organized by pro-ENDS advocacy groups, some of which have links to the nicotine industry (eg, via funding from the "Foundation for a Smoke-Free World"). In 2020, the campaign used dedicated social media accounts to disseminate WVD-branded images and campaign messages. We examined tweets posted as part of WVD 2020 to identify and analyze pro-ENDS policy arguments.

AIMS AND METHODS

We extracted tweets posted between 26 May and 3 June 2020 that included the hashtag #WorldVapeDay. We used qualitative thematic analysis to code a random sample (n = 2200) of approximately half the original English language tweets (n = 4387) and used descriptive analysis to identify the most frequently used co-hashtags.

RESULTS

Arguments related to four themes: harm reduction, smoking cessation, rights and justice, and opposition to ENDS restrictions. Tweets criticized individuals and groups perceived as opposing liberal ENDS regulation, and used personal testimonials to frame ENDS as a harm reduction tool and life-saving smoking cessation aid. Tweets also advanced rights-based arguments, such as privileging adults' rights over children's rights, and calling for greater recognition of consumers' voices. Tweets frequently used hashtags associated with the WHO and World No Tobacco Day (WNTD).

CONCLUSIONS

The WVD campaign presented a series of linked pro-ENDS arguments seemingly aimed at policy-makers, and strategically integrated with the WHO's WNTD campaign. Critically assessing pro-ENDS arguments and the campaigns used to promote these is vital to helping policy actors develop proportionate ENDS policy.

IMPLICATIONS

Social media platforms have considerable potential to influence policy actors. Tweets are easily generated and duplicated, creating an impression of sizeable and influential stakeholders. Evidence that the "World Vape Day" campaign was supported by groups with industry links, and targeted-at least in part-at WHO officials and those who follow the WHO World No Tobacco Day campaign, highlights the importance of critically reviewing such campaigns. Further research could examine how health advocates could engage in pro-ENDS campaigns to support balanced messaging and informed policy-making.

Machine learning applications in tobacco research: a scoping review

OBJECTIVE

Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis.

DATA SOURCES

MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded.

STUDY SELECTION

Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis.

DATA EXTRACTION

Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted.

DATA SYNTHESIS

74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed.

CONCLUSIONS

ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study

BACKGROUND

Although government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts.

OBJECTIVE

We aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery.

METHODS

A total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including #novape, #novaping, #stopvaping, #dontvape, #antivaping, #quitvaping, #antivape, #stopjuuling, #dontvapeonthepizza, and #escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared.

RESULTS

Analyses found that antivaping images of the educational/warning type were the most common (253/500; 50.6%). The average likes of the educational/warning type (15 likes/post) were significantly lower than the catchphrase image type (these emphasized a slogan such as "athletesdontvape" in the image; 32.5 likes/post; P<.001). The majority of the antivaping posts contained the image content element text (n=332, 66.4%), followed by the image content element people/person (n=110, 22%). The images containing people/person elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including "lung health," "teen vaping," "stop vaping," and "vaping death cases." Among the 500 antivaping Instagram posts, while most posts were from the antivaping community (n=177, 35.4%) and personal account types (n=182, 36.4%), the antivaping community account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types.

CONCLUSIONS

Multiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently.

Twitter discourse on nicotine as potential prophylactic or therapeutic for COVID-19

Background

An unproven “nicotine hypothesis” that indicates nicotine's therapeutic potential for COVID-19 has been proposed in recent literature. This study is about Twitter posts that misinterpret this hypothesis to make baseless claims about benefits of smoking and vaping in the context of COVID-19. We quantify the presence of such misinformation and characterize the tweeters who post such messages.

Methods

Twitter premium API was used to download tweets (n = 17,533) that match terms indicating (a) nicotine or vaping themes, (b) a prophylactic or therapeutic effect, and (c) COVID-19 (January-July 2020) as a conjunctive query. A constraint on the length of the span of text containing the terms in the tweets allowed us to focus on those that convey the therapeutic intent. We hand-annotated these filtered tweets and built a classifier that identifies tweets that extrapolate the nicotine hypothesis to smoking/vaping with a positive predictive value of 85%. We analyzed the frequently used terms in author bios, top Web links, and hashtags of such tweets.

Results

21% of our filtered COVID-19 tweets indicate a vaping or smoking-based prevention/treatment narrative. Qualitative analyses show a variety of ways therapeutic claims are being made and tweeter bios reveal pre-existing notions of positive stances toward vaping.

Conclusion

The social media landscape is a double-edged sword in tobacco communication. Although it increases information reach, consumers can also be subject to confirmation bias when exposed to inadvertent or deliberate framing of scientific discourse that may border on misinformation. This calls for circumspection and additional planning in countering such narratives as the COVID-19 pandemic continues to ravage our world. Our results also serve as a cautionary tale in how social media can be leveraged to spread misleading information about tobacco products in the wake of pandemics.

A content analysis of the tweets of e-cigarette proponents in Australia

ISSUE ADDRESSED

Social media sites have become platforms for public discourse on e-cigarettes, providing proponents with an opportunity to disseminate favourable information about the devices. Research examining the information being presented by Australian proponents of e-cigarettes is limited. Accordingly, this study explored the Twitter feeds of Australian proponents of e-cigarettes to determine the nature of the e-cigarette-related content being disseminated.

METHODS

All publicly available e-cigarette-related tweets and retweets (n = 1397) disseminated over a 15-week period by five Australian e-cigarette proponents were captured and analysed.

RESULTS

The main topics covered in the 1397 tweets analysed related to (a) criticism of the arguments made by public health agencies/advocates who oppose e-cigarettes (29%), (b) Australian e-cigarette policy (19%), (c) the health risks of e-cigarettes (16%) and (d) the efficacy of e-cigarettes as smoking cessation aids (13%). Proponents argued that the precautionary principle adopted by public health agencies/advocates lacks an appropriate evidence base and that legalising e-cigarettes would reduce smoking rates and smoking-related harm. Proponents minimised the risks associated with e-cigarette use and only presented evidence indicating that use facilitates smoking cessation.

CONCLUSIONS

The assessed tweets have the potential to reduce the public's trust in the information being presented by authoritative public health agencies/advocates. The dissemination of information downplaying the health risks associated with e-cigarettes may distort perceptions of the devices. SO WHAT?: To assist tobacco control efforts, results highlight the need for (a) ongoing surveillance of the tweets of e-cigarette proponents and (b) provision of evidence-based counterarguments on social media.

Modelling Public Sentiments about Juul Flavors on Twitter through Machine Learning

INTRODUCTION

The availability of a variety of e-cigarettes flavors is one of the frequently cited reasons for their adoption. An active stream of discussion about flavoring can be observed online. Analyzing these real-time conversations offers nuanced insights into key factors related to the adoption of flavors, subsequently supporting public health interventions.

METHODS

Google's BERT, a state-of-the-art deep learning method was employed to model the first sentiment corpus on JUUL flavors. BERT, which is pre-trained with the complete English Wikipedia was fine-tuned by integrating a classification model, with human labeled Tweets, as training data. A collection of 30,075 Tweets about JUUL flavors was classified into positive and negative sentiments. Finally, using topic models, we identify and grouped thematic areas into positive and negative Tweets.

RESULTS

With an average of 89% cross-validation precision for classifying tweets, the finetuned BERT model classified 24,114 Tweets as positive and 5,961 Tweets as negative. Through the topic modeling approach 10 thematic topics were identified from the predicted positive and negative sentiments expressed in the Tweets.

CONCLUSIONS

JUUL flavors, notably mango, mint, and cucumber, provoke overwhelmingly positive sentiments indicating a strong likeness due to favoarble taste and odor. Negative discourse about JUUL flavors revolve around addictiveness, high nicotine content, and youth targeted marketing.

IMPLICATIONS

Limiting the content related to flavors and positive perceptions on social media is necessary to minimize exposure to youth. The novel methodology used in this study may be adopted to monitor e-cigarette discourse periodically, as well as other critical public health phenomena online.

University students' knowledge about and attitudes toward e-cigarette use and factors influencing students' e-cigarette use

Purpose

This study aims to investigate university students' knowledge about and attitudes toward e-cigarette (EC) use. It will also examine whether students' EC use is associated with knowledge about and attitudes toward EC use. The study also aims to analyze the determinants of students' use of ECs. The effects of gender, smoking behavior and friends' and family members' smoking behaviors on students' use of ECs were analyzed.

Design/methodology/approach

Completed questionnaire surveys were received from 1,362 students at King Mongkut's University of Technology Thonburi in Bangkok City, Thailand, in November and December 2019. Chi-square tests and an independent samples t-test were conducted to determine whether students' knowledge about ECs and attitudes toward EC use influenced their use of ECs. A logistic regression analysis was performed to identify significant factors affecting students' use of ECs.

Findings

The results revealed that students' EC use was associated with knowledge about ECs: Students with less knowledge about the harmful effects of ECs were more likely to use them. In addition, students who were EC users had more positive attitudes toward EC use than those who were not EC users. The results also revealed that male students, students who had also smoked tobacco cigarettes and students with friends who smoked tobacco cigarettes were more likely to use ECs. These results could suggest strategies to reduce the use of ECs among university students.

Originality/value

This study provides deep understanding about university students' knowledge about and attitudes toward EC use and their participation in EC use. The result clearly shows university students who are participating in EC use still have less knowledge about EC, thus, they have positive attitudes toward ECs. Gaining social acceptance from friends who use EC also influences students' decision to use EC. Therefore, EC use among students could significantly increase overall EC use.

Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis

INTRODUCTION

As media exposure can influence people's opinions and perceptions about vaping and smoking, analyzing the valence of media content about tobacco products (ie, overall attitude toward tobacco, cigars, electronic cigarettes, etc.) is an important issue. This study advances the field by analyzing a large amount of media content about multiple tobacco products across six different media sources.

AIMS AND METHODS

From May 2014 to December 2017, we collected all English-language media items about tobacco products that U.S. young people might see from mass media and websites (long-form) and social media (Twitter and YouTube). We used supervised machine learning to develop validated algorithms to label the valence of these media items. Using the labeled results, we examined the impact of product type (e-cigarettes vs. other tobacco products), source (long-form vs. social media), and time (by month) on the valence of coverage.

RESULTS

We obtained 152 886 long-form media texts (20% with more than a passing mention), nearly 86 million tweets, and 12 262 YouTube videos about tobacco products. Most long-form media content opposed, while most social media coverage supported, the use of e-cigarettes and other tobacco products. Over time, within-source valence proportions were stable, though in aggregate, the amount of media coverage against the use of tobacco products decreased.

CONCLUSIONS

This study describes the U.S. public communication environment about vaping and smoking for young people and offers a novel big data approach to analyzing media content. Results suggest that content has gradually become less negative toward the use of e-cigarettes and other tobacco products.

IMPLICATIONS

This study is the first to examine how the valence of media coverage differs for e-cigarettes versus other tobacco products, across several media sources, and over time using a large corpus of media items. Unlike prior studies, these data allow us to draw conclusions about relative support and opposition for these two categories of products in a variety of media coverage because the same coding scheme was used across products and media sources.

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.

Public Reactions to the New York State Policy on Flavored E-Cigarettes on Twitter: Observational Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Public Opinion About E-Cigarettes on Chinese Social Media: A Combined Study of Text Mining Analysis and Correspondence Analysis

Background

Electronic cigarettes (e-cigarettes) have become increasingly popular. China has accelerated its legislation on e-cigarettes in recent years by issuing two policies to regulate their use: the first on August 26, 2018, and the second on November 1, 2019. Social media provide an efficient platform to access information on the public opinion of e-cigarettes.

Objective

To gain insight into how policies have influenced the reaction of the Chinese public to e-cigarettes, this study aims to understand what the Chinese public say about e-cigarettes and how the focus of discussion might have changed in the context of policy implementation.

Methods

This study uses a combination of text mining and correspondence analysis to content analyze 1160 e-cigarette–related questions and their corresponding answers from Zhihu, China’s largest question-and-answer platform and one of the country’s most trustworthy social media sources. From January 1, 2017, to December 31, 2019, Python was used to text mine the most frequently used words and phrases in public e-cigarette discussions on Zhihu. The correspondence analysis was used to examine the similarities and differences between high-frequency words and phrases across 3 periods (ie, January 1, 2017, to August 27, 2018; August 28, 2018, to October 31, 2019; and November 1, 2019, to January 1, 2020).

Results

The results of the study showed that the consistent themes across time were comparisons with traditional cigarettes, health concerns, and how to choose e-cigarette products. The issuance of government policies on e-cigarettes led to a change in the focus of public discussion. The discussion of e-cigarettes in period 1 mainly focused on the use and experience of e-cigarettes. In period 2, the public’s attention was not only on the substances related to e-cigarettes but also on the smoking cessation functions of e-cigarettes. In period 3, the public shifted their attention to the e-cigarette industry and government policy on the banning of e-cigarette sales to minors.

Conclusions

Social media are an informative source, which can help policy makers and public health professionals understand the public’s concerns over and understanding of e-cigarettes. When there was little regulation, public discussion was greatly influenced by industry claims about e-cigarettes; however, once e-cigarette policies were issued, these policies, to a large extent, set the agenda for public discussion. In addition, media reporting of these policies might have greatly influenced the way e-cigarette policies were discussed. Therefore, monitoring e-cigarette discussions on social media and responding to them in a timely manner will both help improve the public’s e-cigarette literacy and facilitate the implementation of e-cigarette–related policies.

E-Cigarette Advocates on Twitter: Content Analysis of Vaping-Related Tweets

Background

As the majority of Twitter content is publicly available, the platform has become a rich data source for public health surveillance, providing insights into emergent phenomena, such as vaping. Although there is a growing body of literature that has examined the content of vaping-related tweets, less is known about the people who generate and disseminate these messages and the role of e-cigarette advocates in the promotion of these devices.

Objective

This study aimed to identify key conversation trends and patterns over time, and discern the core voices, message frames, and sentiment surrounding e-cigarette discussions on Twitter.

Methods

A random sample of data were collected from Australian Twitter users who referenced at least one of 15 identified e-cigarette related keywords during 2012, 2014, 2016, or 2018. Data collection was facilitated by TrISMA (Tracking Infrastructure for Social Media Analysis) and analyzed by content analysis.

Results

A sample of 4432 vaping-related tweets posted and retweeted by Australian users was analyzed. Positive sentiment (3754/4432, 84.70%) dominated the discourse surrounding e-cigarettes, and vape retailers and manufacturers (1161/4432, 26.20%), the general public (1079/4432, 24.35%), and e-cigarette advocates (1038/4432, 23.42%) were the most prominent posters. Several tactics were used by e-cigarette advocates to communicate their beliefs, including attempts to frame e-cigarettes as safer than traditional cigarettes, imply that federal government agencies lack sufficient competence or evidence for the policies they endorse about vaping, and denounce as propaganda “gateway” claims of youth progressing from e-cigarettes to combustible tobacco. Some of the most common themes presented in tweets were advertising or promoting e-cigarette products (2040/4432, 46.03%), promoting e-cigarette use or intent to use (970/4432, 21.89%), and discussing the potential of e-cigarettes to be used as a smoking cessation aid or tobacco alternative (716/4432, 16.16%), as well as the perceived health and safety benefits and consequences of e-cigarette use (681/4432, 15.37%).

Conclusions

Australian Twitter content does not reflect the country’s current regulatory approach to e-cigarettes. Rather, the conversation on Twitter generally encourages e-cigarette use, promotes vaping as a socially acceptable practice, discredits scientific evidence of health risks, and rallies around the idea that e-cigarettes should largely be outside the bounds of health policy. The one-sided nature of the discussion is concerning, as is the lack of disclosure and transparency, especially among vaping enthusiasts who dominate the majority of e-cigarette discussions on Twitter, where it is unclear if comments are endorsed, sanctioned, or even supported by the industry.

Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis

Background

On January 1, 2019, a new regulation on the control of smoking in public places was officially implemented in Hangzhou, China. On the day of the implementation, a large number of Chinese media reported the contents of the regulation on the microblog platform Weibo, causing a strong response from and heated discussion among netizens.

Objective

This study aimed to conduct a content and network analysis to examine topics and patterns in the social media response to the new regulation.

Methods

We analyzed all microblogs on Weibo that mentioned and explained the regulation in the first 8 days following the implementation. We conducted a content analysis on these microblogs and used social network visualization and descriptive statistics to identify key users and key microblogs.

Results

Of 7924 microblogs, 12.85% (1018/7924) were in support of the smoking control regulation, 84.12% (6666/7924) were neutral, and 1.31% (104/7924) were opposed to the smoking regulation control. For the negative posts, the public had doubts about the intentions of the policy, its implementation, and the regulations on electronic cigarettes. In addition, 1.72% (136/7924) were irrelevant to the smoking regulation control. Among the 1043 users who explicitly expressed their positive or negative attitude toward the policy, a large proportion of users showed supportive attitudes (956/1043, 91.66%). A total of 5 topics and 11 subtopics were identified.

Conclusions

This study used a content and network analysis to examine topics and patterns in the social media response to the new smoking regulation. We found that the number of posts with a positive attitude toward the regulation was considerably higher than that of the posts with a negative attitude toward the regulation. Our findings may assist public health policy makers to better understand the policy’s intentions, scope, and potential effects on public interest and support evidence-based public health regulations in the future.

Perceptions and Sentiments About Electronic Cigarettes on Social Media Platforms: Systematic Review

Background

Electronic cigarettes (e-cigarettes) have been widely promoted on the internet, and subsequently, social media has been used as an important informative platform by e-cigarette users. Beliefs and knowledge expressed on social media platforms have largely influenced e-cigarette uptake, the decision to switch from conventional smoking to e-cigarette smoking, and positive and negative connotations associated with e-cigarettes. Despite this, there is a gap in our knowledge of people’s perceptions and sentiments on e-cigarettes as depicted on social media platforms.

Objective

This study aimed to (1) provide an overview of studies examining the perceptions and sentiments associated with e-cigarettes on social media platforms and online discussion forums, (2) explore people’s perceptions of e-cigarette therein, and (3) examine the methodological limitations and gaps of the included studies.

Methods

Searches in major electronic databases, including PubMed, Cumulative Index of Nursing and Allied Health Literature, EMBASE, Web of Science, and Communication and Mass Media Complete, were conducted using the following search terms: “electronic cigarette,” “electronic vaporizer,” “electronic nicotine,” and “electronic nicotine delivery systems” combined with “internet,” “social media,” and “internet use.” The studies were selected if they examined participants’ perceptions and sentiments of e-cigarettes on online forums or social media platforms during the 2007-2017 period.

Results

A total of 21 articles were included. A total of 20 different social media platforms and online discussion forums were identified. A real-time snapshot and characteristics of sentiments, personal experience, and perceptions toward e-cigarettes on social media platforms and online forums were identified. Common topics regarding e-cigarettes included positive and negative health effects, testimony by current users, potential risks, benefits, regulations associated with e-cigarettes, and attitude toward them as smoking cessation aids.

Conclusions

Although perceptions among social media users were mixed, there were more positive sentiments expressed than negative ones. This study particularly adds to our understanding of current trends in the popularity of and attitude toward e-cigarettes among social media users. In addition, this study identified conflicting perceptions about e-cigarettes among social media users. This suggests that accurate and up-to-date information on the benefits and risks of e-cigarettes needs to be disseminated to current and potential e-cigarette users via social media platforms, which can serve as important educational channels. Future research can explore the efficacy of social media–based interventions that deliver appropriate information (eg, general facts, benefits, and risks) about e-cigarettes.

Trial Registration

PROSPERO CRD42019121611; https://tinyurl.com/yfr27uxs

Toward an Aggregate, Implicit, and Dynamic Model of Norm Formation: Capturing Large-Scale Media Representations of Dynamic Descriptive Norms Through Automated and Crowdsourced Content Analysis

Media content can shape people's descriptive norm perceptions by presenting either population-level prevalence information or descriptions of individuals' behaviors. Supervised machine learning and crowdsourcing can be combined to answer new, theoretical questions about the ways in which normative perceptions form and evolve through repeated, incidental exposure to normative mentions emanating from the media environment. Applying these methods, this study describes tobacco and e-cigarette norm prevalence and trends over 37 months through an examination of a census of 135,764 long-form media texts, 12,262 popular YouTube videos, and 75,322,911 tweets. Long-form texts mentioned tobacco population norms (4-5%) proportionately less often than e-cigarette population norms (20%). Individual use norms were common across sources, particularly YouTube (tobacco long-form: 34%; Twitter: 33%; YouTube: 88%; e-cigarette long form: 17%; Twitter: 16%; YouTube: 96%). The capacity to capture aggregated prevalence and temporal dynamics of normative media content permits asking population-level media effects questions that would otherwise be infeasible to address.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research

BACKGROUND

Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population. Nearly 70% of US adults use some social media. However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations.

OBJECTIVE

The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments.

METHODS

The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary. The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI. To test the tool's utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter. We conducted a functional correctness analysis of the generated social media messages to assess the algorithm's ability to produce the expected output for each input. We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error. The percent correct was calculated to determine the probability with which the tool generates accurate messages.

RESULTS

The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017). It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques.

CONCLUSIONS

A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction. The tool has the potential to support social media-enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public health organizations with a tool to increase their output of health education messages and manage user comments. We call on readers to use and develop the tool and to contribute to evidence-based communication methods in the digital age.

Tweeting about public health policy: Social media response to the UK Government's announcement of a Parliamentary vote on draft standardised packaging regulations

BACKGROUND

Standardised tobacco packaging has been, and remains, a contentious policy globally, attracting corporate, public health, political, media and popular attention. In January 2015, the UK Government announced it would vote on draft regulations for the policy before the May 2015 General Election. We explored reactions to the announcement on Twitter, in comparison with an earlier period of little UK Government activity on standardised packaging.

METHODS

We obtained a random sample of 1038 tweets in two 4-week periods, before and after the UK Government's announcement. Content analysis was used to examine the following Tweet characteristics: support for the policy, purpose, Twitter-user's geographical location and affiliation, and evidence citation and quality. Chi-squared analyses were used to compare Tweet characteristics between the two periods.

RESULTS

Overall, significantly more sampled Tweets were in favour of the policy (49%) in comparison to those opposed (19%). Yet, at Time 2, following the announcement, a greater proportion of sampled tweets opposed standardised packaging compared to the period sampled at Time 1, prior to the announcement (p<0.001). The quality of evidence and research cited in URLs linked at Time 2 was significantly lower than at Time 1 (p<0.001), with peer-reviewed research more likely to be shared in positive Tweets (p<0.001) and in Tweets linking to URLs originating from the health sector (p<0.001). The decline in the proportion of positive Tweets was mirrored by a reduction in Tweets by health sector Twitter-users at Time 2 (p<0.001).

CONCLUSIONS

Microblogging sites can reflect offline policy debates and are used differently by policy proponents and opponents dependent on the policy context. Twitter-users opposed to standardised packaging increased their activity following the Government's announcement, while those in support broadly maintained their rate of Twitter engagement. The findings offer insight into the public health community's options for using Twitter to influence policy and disseminate research. In particular, proliferation of Twitter activity following pro-public health policy announcements could be considered to ensure pro-health messages are not overshadowed by anti-regulation voices.

The Messages Presented in Electronic Cigarette-Related Social Media Promotions and Discussion: Scoping Review

BACKGROUND

There has been a rapid rise in the popularity of electronic cigarettes (e-cigarettes) over the last decade, with growth predicted to continue. The uptake of these devices has escalated despite inconclusive evidence of their efficacy as a smoking cessation device and unknown long-term health consequences. As smoking rates continue to drop or plateau in many well-developed countries, transnational tobacco companies have transitioned into the vaping industry and are now using social media to promote their products. Evidence indicates e-cigarettes are being marketed on social media as a harm reduction alternative, with retailers and manufacturers utilizing marketing techniques historically used by the tobacco industry.

OBJECTIVE

This study aimed to identify and describe the messages presented in e-cigarette-related social media (Twitter, YouTube, Instagram, and Pinterest) promotions and discussions and identify future directions for research, surveillance, and regulation.

METHODS

Data sources included MEDLINE, Scopus, ProQuest, Informit, the Journal of Medical Internet Research, and Google Scholar. Included studies were published in English between 2007 and 2017, analyzed content captured from e-cigarette-related social media promotions or discussions, and reported results for e-cigarettes separately from other forms of tobacco and nicotine delivery. Database search ceased in October 2017. Initial searches identified 536 studies. Two reviewers screened studies by title and abstract. One reviewer examined 71 full-text articles to determine eligibility and identified 25 studies for inclusion. This process was undertaken with the assistance of the Web-based screening and data extraction tool-Covidence. The review was registered with the Joanna Briggs Institute (JBI) Systematic Reviews database and followed the methodology for JBI Scoping Reviews.

RESULTS

Several key messages are being used to promote e-cigarettes including as a safer alternative to cigarettes, efficacy as a smoking cessation aid, and for use where smoking is prohibited. Other major marketing efforts aimed at capturing a larger market involve promotion of innovative flavoring and highlighting the public performance of vaping. Discussion and promotion of these devices appear to be predominantly occurring among the general public and those with vested interests such as retailers and manufacturers. There is a noticeable silence from the public health and government sector in these discussions on social media.

CONCLUSIONS

The social media landscape is dominated by pro-vaping messages disseminated by the vaping industry and vaping proponents. The uncertainty surrounding e-cigarette regulation expressed within the public health field appears not to be reflected in ongoing social media dialogues and highlights the need for public health professionals to interact with the public to actively influence social media conversations and create a more balanced discussion. With the vaping industry changing so rapidly, real-time monitoring and surveillance of how these devices are discussed, promoted, and used on social media is necessary in conjunction with evidence published in academic journals.

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.

"Okay, We Get It. You Vape": An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter

The current study examined conversations on Twitter related to use and perceptions of e-cigarettes in the United States. We employed the Social Media Analytic and Research Testbed (SMART) dashboard, which was used to identify and download (via a public API) e-cigarette-related geocoded tweets. E-cigarette-related tweets were collected continuously using customized geo-targeted Twitter APIs. A total of 193,051 tweets were collected between October 2015 and February 2016. Of these tweets, a random sample of 973 geocoded tweets were selected and manually coded for information regarding source, context, and message characteristics. Our findings reveal that although over half of tweets were positive, a sizeable portion was negative or neutral. We also found that, among those tweets mentioning a stigma of e-cigarettes, most confirmed that a stigma does exist. Conversely, among tweets mentioning the harmfulness of e-cigarettes, most denied that e-cigarettes were a health hazard. These results suggest that current efforts have left the public with ambiguity regarding the potential dangers of e-cigarettes. Consequently, it is critical to communicate the public health stance on this issue to inform the public and provide counterarguments to the positive sentiments presently dominating conversations about e-cigarettes on social media. The lack of awareness and need to voice a public health position on e-cigarettes represents a vital opportunity to continue winning gains for tobacco control and prevention efforts through health communication interventions targeting e-cigarettes.

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.

Whose Post Is It? Predicting E-cigarette Brand from Social Media Posts

OBJECTIVES

E-cigarette advertisers know that 76% of youth use social media, yet little is known about the nature of e-cigarette advertising on social media most favored by youth. We utilized text-mining to characterize e-cigarette advertising and marketing messages from image-focused social media brand sites, and to construct and test an algorithm for predicting brand from brand-generated social media posts.

METHODS

Data comprised 5022 unique posts accompanied by an image from Facebook, Instagram or Pinterest e-cigarette brand pages for Blu, Logic, Metro, and NJOY from February 2012 to April 2015. Text-tokenization was used to quantify text for use as predictors in analyses.

RESULTS

Blu had the largest social media presence (65%), followed by Logic (16%), NJOY (12%) and Metro (7%). Blu's average post length was significantly shorter than all other brands. Words most commonly used in posts differed by brand. Regression analyses successfully differentiated Blu and NJOY brands from other brands.

CONCLUSIONS

Analyses revealed e-cigarette brands used different types of messages to appeal to social media users. Whereas words used by Blu and NJOY sold a "lifestyle," words used by Logic and Metro relied on device and product identification.

Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings

Biomedical word sense disambiguation (WSD) is an important intermediate task in many natural language processing applications such as named entity recognition, syntactic parsing, and relation extraction. In this paper, we employ knowledge-based approaches that also exploit recent advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the public MSH WSD dataset [1] as the test set. Our methods involve weak supervision - we do not use any hand-labeled examples for WSD to build our prediction models; however, we employ an existing concept mapping program, MetaMap, to obtain our concept vectors. Over the MSH WSD dataset, our linear time (in terms of numbers of senses and words in the test instance) method achieves an accuracy of 92.24% which is a 3% improvement over the best known results [2] obtained via unsupervised means. A more expensive approach that we developed relies on a nearest neighbor framework and achieves accuracy of 94.34%, essentially cutting the error rate in half. Employing dense vector representations learned from unlabeled free text has been shown to benefit many language processing tasks recently and our efforts show that biomedical WSD is no exception to this trend. For a complex and rapidly evolving domain such as biomedicine, building labeled datasets for larger sets of ambiguous terms may be impractical. Here, we show that weak supervision that leverages recent advances in representation learning can rival supervised approaches in biomedical WSD. However, external knowledge bases (here sense inventories) play a key role in the improvements achieved.

Classification of Twitter Users Who Tweet About E-Cigarettes

Background

Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette–related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette–related content on Twitter.

Objective

The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette–related topics into distinct categories.

Methods

We collected approximately 11.5 million e‑cigarette–related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles’ profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users’ metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types.

Results

Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F₁ score=83.3%). Accuracy varied by user type, with F₁ scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance—an overall F₁ score gain of 10.6%—beyond metadata features alone.

Conclusions

This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.

Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health Apps

Objective: To summarize the state of the art during the year 2016 in the areas related to consumer health informatics and education with a special emphasis in secondary use of patient data. Methods: We conducted a systematic review of articles published in 2016, using PubMed with a predefined set of queries. We identified over 320 potential articles for review. Papers were considered according to their relevance for the topic of the section. Using consensus, we selected the 15 most representative papers, which were submitted to external reviewers for full review and scoring. Based on the scoring and quality criteria, five papers were finally selected as best papers Results: The five best papers can be grouped in two major areas: 1) methods and tools to identify and collect formal requirements for secondary use of data, and 2) innovative topics highlighting the interest of carrying on "secondary" studies on patient data, more specifically on the data self-expressed by patients through social media tools. Regarding the formal requirements about informed consent, the selected papers report a comparison of legal aspects in European countries to find a common and unified grammar around the concept of "data donation". Regarding innovative approaches to value patient data, the selected papers report machine learning algorithms to extract knowledge from patient experience and satisfaction with health care delivery, drug and medication use, treatment compliance and barriers during cancer disease, or acceptation of public health actions such as vaccination. Conclusions: Secondary use of patient data (apart from personal health care record data) can be expressed according to many ways. Requirements to allow this secondary use have to be harmonized between countries, and social media platforms can be efficiently used to explore and create knowledge on patient experience with health problems or activities. Machine learning algorithms can explore those massive amounts of data to support health care professionals, and institutions provide more accurate knowledge about use and usage, behaviour, sentiment, or satisfaction about health care delivery.

Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users

Large shifts in medical, recreational, and illicit cannabis consumption in the United States have implications for personalizing treatment and prevention programs to a wide variety of populations. As such, considerable research has investigated clinical presentations of cannabis users in clinical and population-based samples. Studies leveraging big data, social media, and social network analysis have emerged as a promising mechanism to generate timely insights that can inform treatment and prevention research. This study extends a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter. A set of examples illustrate how this method can ascertain candidate samples of medical, recreational, and illicit cannabis users. Implications for research planning, intervention design, and public health surveillance are discussed.

On Quantifying Diffusion of Health Information on Twitter

particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

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.

Public reactions to e-cigarette regulations on Twitter: a text mining analysis

BACKGROUND

In May 2016, the Food and Drug Administration (FDA) issued a final rule that deemed e-cigarettes to be within their regulatory authority as a tobacco product. News and opinions about the regulation were shared on social media platforms, such as Twitter, which can play an important role in shaping the public's attitudes. We analysed information shared on Twitter for insights into initial public reactions.

METHODS

A text mining approach was used to uncover important topics among reactions to the e-cigarette regulations on Twitter. SAS Text Miner V.12.1 software was used for descriptive text mining to uncover the primary topics from tweets collected from May 1 to May 17 2016 using NUVI software to gather the data.

RESULTS

A total of nine topics were generated. These topics reveal initial reactions to whether the FDA's e-cigarette regulations will benefit or harm public health, how the regulations will impact the emerging e-cigarette market and efforts to share the news. The topics were dominated by negative or mixed reactions.

CONCLUSIONS

In the days following the FDA's announcement of the new deeming regulations, the public reaction on Twitter was largely negative. Public health advocates should consider using social media outlets to better communicate the policy's intentions, reach and potential impact for public good to create a more balanced conversation.

Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015

The reasons for using electronic nicotine delivery systems (ENDS) are poorly understood and are primarily documented by expensive cross-sectional surveys that use preconceived close-ended response options rather than allowing respondents to use their own words. We passively identify the reasons for using ENDS longitudinally from a content analysis of public postings on Twitter. All English language public tweets including several ENDS terms (e.g., “e-cigarette” or “vape”) were captured from the Twitter data stream during 2012 and 2015. After excluding spam, advertisements, and retweets, posts indicating a rationale for vaping were retained. The specific reasons for vaping were then inferred based on a supervised content analysis using annotators from Amazon’s Mechanical Turk. During 2012 quitting combustibles was the most cited reason for using ENDS with 43% (95%CI 39–48) of all reason-related tweets cited quitting combustibles, e.g., “I couldn’t quit till I tried ecigs,” eclipsing the second most cited reason by more than double. Other frequently cited reasons in 2012 included ENDS’s social image (21%; 95%CI 18–25), use indoors (14%; 95%CI 11–17), flavors (14%; 95%CI 11–17), safety relative to combustibles (9%; 95%CI 7–11), cost (3%; 95%CI 2–5) and favorable odor (2%; 95%CI 1–3). By 2015 the reasons for using ENDS cited on Twitter had shifted. Both quitting combustibles and use indoors significantly declined in mentions to 29% (95%CI 24–33) and 12% (95%CI 9–16), respectively. At the same time, social image increased to 37% (95%CI 32–43) and lack of odor increased to 5% (95%CI 2–5), the former leading all cited reasons in 2015. Our data suggest the reasons people vape are shifting away from cessation and toward social image. The data also show how the ENDS market is responsive to a changing policy landscape. For instance, smoking indoors was less frequently cited in 2015 as indoor smoking restrictions became more common. Because the data and analytic approach are scalable, adoption of our strategies in the field can inform follow-up survey-based surveillance (so the right questions are asked), interventions, and policies for ENDS.

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

Background

As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product.

Objective

This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users.

Methods

Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter’s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation.

Results

While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices.

Conclusions

These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes.

Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter

Electronic cigarettes (e-cigs) have been gaining popularity and have emerged as a controversial tobacco product since their introduction in 2007 in the U.S. The smoke-free aspect of e-cigs renders them less harmful than conventional cigarettes and is one of the main reasons for their use by people who plan to quit smoking. The US food and drug administration (FDA) has introduced new regulations early May 2016 that went into effect on August 8, 2016. Given this important context, in this paper, we report results of a project to identify current themes in e-cig tweets in terms of semantic interpretations of topics generated with topic modeling. Given marketing/advertising tweets constitute almost half of all e-cig tweets, we first build a classifier that identifies marketing and non-marketing tweets based on a hand-built dataset of 1000 tweets. After applying the classifier to a dataset of over a million tweets (collected during 4/2015 - 6/2016), we conduct a preliminary content analysis and run topic models on the two sets of tweets separately after identifying the appropriate numbers of topics using topic coherence. We interpret the results of the topic modeling process by relating topics generated to specific e-cig themes. We also report on themes identified from e-cig tweets generated at particular places (such as schools and churches) for geo-tagged tweets found in our dataset using the GeoNames API. To our knowledge, this is the first effort that employs topic modeling to identify e-cig themes in general and in the context of geo-tagged tweets tied to specific places of interest.

Classification of Helpful Comments on Online Suicide Watch Forums

Among social media websites, Reddit has emerged as a widely used online message board for focused mental health topics including depression, addiction, and suicide watch (SW). In particular, the SW community/subreddit has nearly 40,000 subscribers and 13 human moderators who monitor for abusive comments among other things. Given comments on posts from users expressing suicidal thoughts can be written from any part of the world at any time, moderating in a timely manner can be tedious. Furthermore, Reddit's default comment ranking does not involve aspects that relate to the "helpfulness" of a comment from a suicide prevention (SP) perspective. Being able to automatically identify and score helpful comments from such a perspective can assist moderators, help SW posters to have immediate feedback on the SP relevance of a comment, and also provide insights to SP researchers for dealing with online aspects of SP. In this paper, we report what we believe is the first effort in automatic identification of helpful comments on online posts in SW forums with the SW subreddit as the use-case. We use a dataset of 3000 real SW comments and obtain SP researcher judgments regarding their helpfulness in the contexts of the corresponding original posts. We conduct supervised learning experiments with content based features including n-grams, word psychometric scores, and discourse relation graphs and report encouraging F-scores (≈ 80 - 90%) for the helpful comment classes. Our results indicate that machine learning approaches can offer complementary moderating functionality for SW posts. Furthermore, we realize assessing the helpfulness of comments on mental health related online posts is a nuanced topic and needs further attention from the SP research community.