Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter

Tobacco Risk Information and Comparative Risk Assessment of E-Cigarettes Vs. Cigarettes: Application of the Reinforcing Spirals Model

We examine how individuals seek information about e-cigarette risk and selectively expose themselves to media sources that may provide information consistent with their existing beliefs and behaviors related to e-cigarettes in application of the Reinforcing Spirals Model (Slater, 2007). Additionally, the associations among e-cigarette risk information-seeking, tobacco risk information exposure via media, and comparative risk assessment of e-cigarettes versus conventional cigarettes were identified. The results were compared among current users, former users, and those who had never used e-cigarettes ("never users"). A nationally representative data from the 2017 Health Information National Trends Surveys - FDA was employed. Our analyses suggest the presence of comparative risk perception among current users as assessing the health harm and addiction risk of e-cigarettes significantly lower than cigarettes. Current users did not avoid information about the health effects of e-cigarettes; they were more likely to look for such information than former and never users. Current users' e-cigarette risk information-seeking was negatively associated with the difference between perceived addiction risk of e-cigarettes and that of cigarettes. Current users' tobacco risk information exposure via social media was related with a lower level of the perceived risk of e-cigarettes, while such exposure via news media was related with a higher level of perceived risk of e-cigarettes. Our findings suggest the need for regulating and counteracting the prevalent e-cigarette information that discounts the relative risks of e-cigarettes as well as the potential of news media as influential sources helping current users scrutinize the risks.

Understanding public opinion to the introduction of minimum unit pricing in Scotland: a qualitative study using Twitter

OBJECTIVES

On 1 May 2018 minimum unit pricing (MUP) of alcohol was introduced in Scotland. This study used Twitter posts to quantify sentiment expressed online during the introduction of MUP, conducted a thematic analysis of these perceptions and analysed which Twitter users were associated with which particular sentiments.

DESIGN AND SETTING

This qualitative social media analysis captured all tweets relating to MUP during the 2 weeks after the introduction of the policy. These tweets were assessed using a mixture of human and machine coding for relevance, sentiment and source. A thematic analysis was conducted.

PARTICIPANTS

74 639 tweets were collected over 14 days. Of these 53 574 were relevant to MUP.

RESULTS

Study findings demonstrate that opinion on the introduction of MUP in Scotland was somewhat divided, as far as is discernible on Twitter, with a slightly higher proportion of positive posts (35%) than negative posts (28%), with positive sentiment stronger in Scotland itself. Furthermore, 55% of positive tweets/retweets were originally made by health or alcohol policy-related individuals or organisations. Thematic analysis of tweets showed some evidence of misunderstanding around policy issues.

CONCLUSIONS

It is possible to appreciate the divided nature of public opinion on the introduction of MUP in Scotland using Twitter, the nature of the sentiment around it and the key actors involved. It will be possible to later study how this changes when the policy becomes more established.

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

BACKGROUND

Tobacco-related content on social media is generated and propagated by opinion leaders on the Web who disseminate messages to others in their network, including followers, who then continue to spread the information. Opinion leaders can exert powerful influences on their followers' knowledge, attitudes, and behaviors; yet, little is known about the demographic characteristics and tobacco use behavior of tobacco opinion leaders on the Web and their followers, compared with general Twitter users.

OBJECTIVE

In this study, we hypothesized that opinion leaders use more tobacco products and have higher nicotine dependence than the other 2 groups (eg, followers and general Twitter users) and that followers-those who spread messages by opinion leaders-would more likely be in demographic groups that are vulnerable to tobacco marketing influence (eg, young adults and lower educational attainment).

METHODS

We constructed the social networks of people who tweet about tobacco and categorized them using a combination of social network and Twitter metrics. To understand the characteristics of tobacco opinion leaders and their followers, we conducted a survey of tobacco opinion leaders, their followers, and general Twitter users. The sample included 347 opinion leaders, 567 followers, and 519 general users. The opinion leaders had a median of 1000 followers, whereas followers and general users had fewer than 600 followers.

RESULTS

Opinion leaders were more likely than their followers to report past month use of tobacco products; followers, in turn, were more likely to report past month use of these products than general Twitter users. The followers appeared to be an especially vulnerable group; they tended to be younger (mean age 22.4 years) and have lower education compared with the opinion leaders and general users.

CONCLUSIONS

Followers of Twitter tobacco opinion leaders are a vulnerable group that might benefit from antitobacco education to counter the protobacco communications they see on social media.

A longitudinal analysis of electronic cigarette forum participation

Electronic cigarette (e-cigarette) Internet forums are social networking websites that allow members around the world to share information and personal experiences about e-cigarettes. However, little is known about their popularity and reach. This longitudinal study investigated indicators of participation (membership, number of discussion boards, and number of message posts) in 77 e-cigarette forums from February 2016 to January 2017. Autoregressive time-series analyses were used to assess monthly changes in participation among small (<1000 members), medium (1000-5000 members), and large (>5000 members) forums that were active at all 12 observation time points. Paired sample t-tests were conducted to compare changes in participation before and after the U.S. Food and Drug Administration announced the regulation of e-cigarettes. Analyses revealed significant and consistent increases in forum membership (R²s > 0.72; ps < 0.01), discussion boards (R²s > 0.97; ps < 0.001), and message posts (R²s > 0.96; ps < 0.001) across all forum sizes during the 12-month period. No differences were found in participation indicators pre- and post-regulation announcement. E-cigarette forums provide real-time information on the levels of participation over time and therefore may be an important platform to study interactions among consumers and how these interactions shape e-cigarette knowledge, attitudes and use behaviors.

Does Seeking e-Cigarette Information Lead to Vaping? Evidence from a National Longitudinal Survey of Youth and Young Adults

Youth and young adults (YYAs) are vulnerable populations for e-cigarette use or vaping. This study examined the effect of YYAs' health information seeking behavior (HISB) around e-cigarette use and vaping on their subsequent vaping behavior. We conducted a nationally representative longitudinal phone survey of 13-25 year olds from June 2014 to September 2016, with 2,413 respondents who completed a baseline and follow-up survey six months later. The results from lagged logistic regressions and mediation analyses showed a) that information seeking predicted higher likelihood of vaping six months later even after controlling for baseline smoking and vaping status, intention to vape, and demographics, and b) that information seeking partially mediated the relationship between intention to vape and subsequent vaping behavior. Theoretical and regulatory implications are discussed.

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.

Electronic cigarette usage patterns: a case study combining survey and social media data

Objective

To identify who were social media active e-cigarette users, to compare the use patterns from both survey and social media data for data triangulation, and to jointly use both datasets to conduct a comprehensive analysis on e-cigarette future use intentions.

Materials and Methods

We jointly used an e-cigarette use online survey (n = 5132) and a social media dataset. We conducted analysis from 3 different perspectives. We analyzed online forum participation patterns using survey data. We compared e-cigarette use patterns, including brand and flavor types, ratings, and purchase approaches, between the 2 datasets. We used logistic regression to study intentions to use e-cigarettes using both datasets.

Results

Male and younger e-cigarette users were the most likely to participate in e-cigarette-related discussion forums. Forum active survey participants were hardcore vapers. The e-cigarette use patterns were similar in the online survey data and the social media data. Intention to use e-cigarettes was positively related to e-liquid ratings and flavor ratings. Social media provided a valuable source of information on users' ratings of e-cigarette refill liquids.

Discussion

For hardcore vapers, social media data were consistent with online survey data, which suggests that social media may be useful to study e-cigarette use behaviors and can serve as a useful complement to online survey research. We proposed an innovative framework for social media data triangulation in public health studies.

Conclusion

We illustrated how social media data, combined with online survey data, can serve as a new and rich information source for public health research.

Relationship between social media engagement and e-cigarette policy support

Introduction

Given increasing efforts to regulate e-cigarettes, it is important to understand factors associated with support for tobacco regulatory policies. We investigate such factors found in social media and hypothesize that greater online engagement with tobacco content would be associated with less support for e-cigarette regulatory policies.

Methods

We constructed social networks of Twitter users who tweet about tobacco and categorized them using a combination of social network and Twitter metrics. Twitter users were identified as representing leaders, followers or general users in online discussions of tobacco products, and invited to complete an online survey. Participants responded to questions about their engagement with tobacco-related content online, degree of support for e-cigarette regulations, exposure to tobacco marketing, e-cigarette use and other demographic information. We examined links between their reported engagement with tobacco-related content and support for e-cigarette regulatory policies using structural equation modelling.

Results

The analytic sample consisted of 470 participants. The conceptualized structural equation model had a good fit (χ² (32) = 24.85, p = 0.09, CFI = 0.99, RMSEA = 0.03). Findings support our hypothesis: engagement with online tobacco content was negatively associated with support for e-cigarette policies, while controlling for e-cigarette use, tobacco marketing exposure, social media use frequency and demographic factors.

Conclusions

Findings suggest that our hypothesis was supported. Twitter users engaging with tobacco-related content and harboring negative attitudes toward e-cigarette regulatory policies could be an important audience segment to reach with tailored e-cigarette policy education messages.

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Online engagement associated with lower support for e-cigarette regulations.

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E-cigarette use, sex and income associated with support for e-cigarette regulations.

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Tobacco marketing exposure not associated with e-cigarette regulation support.

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Findings inform tobacco policy education campaign efforts.

Exposure to positive peer sentiment about nicotine replacement therapy in an online smoking cessation community is associated with NRT use

BACKGROUND

Little is known about the influence of online peer interactions on health behavior change. This study examined the relationship between exposure to peer sentiment about nicotine replacement therapy (NRT) in an online social network for smoking cessation and NRT use.

METHODS

Participants were 3297 current smokers who enrolled in an Internet smoking cessation program, participated in a randomized trial, and completed a 3-month follow-up. Half received free NRT as part of the trial. Automated text classification identified 27,038 posts about NRT that one or more participants were exposed to in the social network. Sentiment towards NRT was rated on Amazon Mechanical Turk. Participants' exposure to peer sentiment about NRT was determined by analysis of clickstream data. Modified Poisson regression examined self-reported use of NRT at 3-months as a function of exposure to NRT sentiment, controlling for study arm and post exposure.

RESULTS

One in five participants (19.3%, n = 639) were exposed to any NRT-related posts (mean exposure = 6.5 ± 14.7, mean sentiment = 5.4 ± 0.8). The association between sentiment exposure and NRT use varied by receipt of free NRT. Greater exposure to positive NRT sentiment was associated with an increased likelihood of NRT use among participants who did not receive free NRT (adjusted rate ratio 1.22, 95% CI 1.01, 1.47; p = .043), whereas no such relationship was observed among participants who did receive free NRT (p = .48).

CONCLUSIONS

Exposure to positive sentiment about NRT was associated with increased NRT use when smokers obtained it on their own. Highlighting user-generated content containing positive NRT sentiment may increase NRT use among treatment-seeking smokers.

Predicting the success of Twitter in healthcare

Purpose

The purpose of this paper is to predict Twitter satisfaction by healthcare professionals through integrating constructs of Csikszentmihalyi’s flow theory, quality dimensions and usefulness.

Design/methodology/approach

Survey responses of 108 physicians from a variety of specialisations in the United Arab Emirates have been validated and analysed by means of partial least squares-based structural equation modelling method using smartPLS software.

Findings

Service quality has emerged as the most influential quality dimension that positively impact flow state and perceived usefulness of Twitter, while information quality, surprisingly, does not show any effect. The findings also indicate that flow state plays a significant role in shaping physicians’ satisfaction with Twitter. The study also enhances our understanding concerning the effects of perceived usefulness on flow state and satisfaction.

Research limitations/implications

Understanding factors that influence Twitter satisfaction can help healthcare managers construct appropriate intervention strategies for maximising professional benefits of social media and minimising user resistance. This is important because top managers usually ratify traditional practices that are only of limited effect. Also, the findings help vendors to accentuate user’s concerns in addition to system functionalities in social media applications.

Originality/value

The paper is an early attempt to propose a model for social media success in a professional context in general and healthcare in particular. It also one of first studies that examine social media satisfaction through integrating contemporary information system success and acceptance models with flow theory.

Visual Cancer Communication on Social Media: An Examination of Content and Effects of #Melanomasucks

Background

Instagram is increasingly becoming a platform on which visual communication of cancer takes place, but few studies have investigated the content and effects. In particular, a paucity of research has evaluated the effects of visual communication of cancer on participative engagement outcomes.

Objective

The objective of our study was to investigate cancer-related beliefs and emotions shared on Instagram and to examine their effects on participative engagement outcomes including likes, comments, and social support.

Methods

This study analyzed the content of 441 posts of #melanomasucks on Instagram and assessed the effects of the content characteristics on outcomes, including the number of likes and comments and types of social support using group least absolute shrinkage and selection operator logistic regression.

Results

Posts about controlling melanoma were most frequent (271/441, 61.5%), followed by 240 (54.4%) posts about outcomes of having melanoma. Ninety posts (20.4%) were about the causes of melanoma. A greater number of posts expressed positive (159/441, 36.1%) than negative emotions (100/441, 22.7%). Eighty posts (18.1%) expressed hope, making it the most frequently expressed emotion; 49 posts expressed fear (11.1%), 46 were humorous (10.4%), and 46 showed sadness (10.4%). Posts about self behavior as a cause of melanoma decreased likes (P<.001) and social support comments (P=.048). Posts about physical consequences of melanoma decreased likes (P=.02) but increased comments (P<.001) and emotional social support (P<.001); posts about melanoma treatment experience increased comments (P=.03) and emotional social support (P<.001). None of the expressions of positive emotions increased likes, comments, or social support. Expression of anger increased the number of likes (P<.001) but those about fear (P<.001) and joy (P=.006) decreased the number of likes. Posts about fear (P=.003) and sadness (P=.003) increased emotional social support. Posts showing images of melanoma or its treatment on the face or body parts made up 21.8% (96/441) of total posts. Inclusion of images increased the number of comments (P=.001).

Conclusions

To our knowledge, this is the first investigation of the content and effects of user-generated visual cancer communication on social media. The findings show where the self-expressive and social engagement functions of #melanomasucks converge and diverge, providing implications for extending research on the commonsense model of illness and for developing conceptual frameworks explaining participative engagement on social media.

Understanding Users' Vaping Experiences from Social Media: Initial Study Using Sentiment Opinion Summarization Techniques

Background

E-liquid is one of the main components in electronic nicotine delivery systems (ENDS). ENDS review comments could serve as an early warning on use patterns and even function to serve as an indicator of problems or adverse events pertaining to the use of specific e-liquids—much like types of responses tracked by the Food and Drug Administration (FDA) regarding medications.

Objective

This study aimed to understand users’ “vaping” experience using sentiment opinion summarization techniques, which can help characterize how consumers think about specific e-liquids and their characteristics (eg, flavor, throat hit, and vapor production).

Methods

We collected e-liquid reviews on JuiceDB from June 27, 2013 to December 31, 2017 using its public application programming interface. The dataset contains 27,070 reviews for 8058 e-liquid products. Each review is accompanied by an overall rating and a set of 4 aspect ratings of an e-liquid, each on a scale of 1-5: flavor accuracy, throat hit, value, and cloud production. An iterative dichotomiser 3 (ID3)-based influential aspect analysis model was adopted to learn the key elements that impact e-liquid use. Then, fine-grained sentiment analysis was employed to mine opinions on various aspects of vaping experience related to e-liquids.

Results

We found that flavor accuracy and value were the two most important aspects that affected users’ sentiments toward e-liquids. Of reviews in JuiceDB, 67.83% (18,362/27,070) were positive, while 12.67% (3430/27,070) were negative. This indicates that users generally hold positive attitudes toward e-liquids. Among the 9 flavors, fruity and sweet were the two most popular. Great and sweet tastes, reasonable value, and strong throat hit made users satisfied with fruity and sweet flavors, whereas “strange” tastes made users dislike those flavors. Meanwhile, users complained about some e-liquids’ steep or expensive prices, bad quality, and harsh throat hit. There were 2342 fruity e-liquids and 2049 sweet e-liquids. There were 55.81% (1307/2342) and 59.83% (1226/2049) positive sentiments and 13.62% (319/2342) and 12.88% (264/2049) negative sentiments toward fruity e-liquids and sweet e-liquids, respectively. Great flavors and good vapors contributed to positive reviews of fruity and sweet products. However, bad tastes such as “sour” or “bitter” resulted in negative reviews. These findings can help businesses and policy makers to further improve product quality and formulate effective policy.

Conclusions

This study provides an effective mechanism for analyzing users’ ENDS vaping experience based on sentiment opinion summarization techniques. Sentiment opinions on aspect and products can be found using our method, which is of great importance to monitor e-liquid products and improve work efficiency.

Reproductive Health and Medication Concerns for Patients With Inflammatory Bowel Disease: Thematic and Quantitative Analysis Using Social Listening

BACKGROUND

Inflammatory bowel disease (IBD) affects many individuals of reproductive age. Most IBD medications are safe to use during pregnancy and breastfeeding; however, observational studies find that women with IBD have higher rates of voluntary childlessness due to fears about medication use during pregnancy. Understanding why and how individuals with IBD make decisions about medication adherence during important reproductive periods can help clinicians address patient fears about medication use.

OBJECTIVE

The objective of this study was to gain a more thorough understanding of how individuals taking IBD medications during key reproductive periods make decisions about their medication use.

METHODS

We collected posts from 3000 social media sites posted over a 3-year period and analyzed the posts using qualitative descriptive content analysis. The first level of analysis, open coding, identified individual concepts present in the social media posts. We subsequently created a codebook from significant or frequently occurring codes in the data. After creating the codebook, we reviewed the data and coded using our focused codes. We organized the focused codes into larger thematic categories.

RESULTS

We identified 7 main themes in 1818 social media posts. Individuals used social media to (1) seek advice about medication use related to reproductive health (13.92%, 252/1818); (2) express beliefs about the safety of IBD therapies (7.43%, 135/1818); (3) discuss personal experiences with medication use (16.72%, 304/1818); (4) articulate fears and anxieties about the safety of IBD therapies (11.55%, 210/1818); (5) discuss physician-patient relationships (3.14%, 57/1818); (6) address concerns around conception, infertility, and IBD medications (17.38%, 316/1818); and (7) talk about IBD symptoms during and after pregnancy and breastfeeding periods (11.33%, 206/1818).

CONCLUSIONS

Beliefs around medication safety play an important role in whether individuals with IBD decide to take medications during pregnancy and breastfeeding. Having a better understanding about why patients stop or refuse to take certain medications during key reproductive periods may allow clinicians to address specific beliefs and attitudes during office visits.

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.

Trends of infodemiology studies: a scoping review

INTRODUCTION

The health care industry is rich in data and information. Web technologies, such as search engines and social media, have provided an opportunity for the management of user generated data in real time in the form of infodemiology studies. The aim of this study was to investigate infodemiology studies conducted during 2002-2016, and compare them based on developed, developing and in transition countries.

METHODS

This scoping review was conducted in 2017 with the help of the PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Knowledge, Google Scholar, Wiley and Springer databases were searched between the years 2002 and 2016. Finally, 56 articles were included in the review and analysed.

RESULTS

The initial infodemiology studies pertain to the quality assessment of the hospital's websites. Most of the studies were on developed countries, based on flu, and published in the Journal of Medical Internet Research.

CONCLUSION

The infodemiology approach provides unmatched opportunities for the management of health data and information generated by the users. Using this potential will provide unique opportunities for the health information need assessment in real time by health librarians and thereby provide evidence based health information to the people.

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.

'Sweeter Than a Swisher': amount and themes of little cigar and cigarillo content on Twitter

Objective

Despite recent increases in little cigar and cigarillo (LCC) use—particularly among urban youth, African-Americans and Latinos—research on targeted strategies for marketing these products is sparse. Little is known about the amount or content of LCC messages users see or share on social media, a popular communication medium among youth and communities of colour.

Methods

Keyword rules were used to collect tweets related to LCCs from the Twitter Firehose posted in October 2014 and March–April 2015. Tweets were coded for promotional content, brand references, co-use with marijuana and subculture references (eg, rap/hip-hop, celebrity endorsements) and were classified as commercial and ‘organic’/non-commercial using a combination of machine learning methods, keyword algorithms and human coding. Metadata associated with each tweet were used to categorise users as influencers (1000 and more followers) and regular users (under 1000 followers).

Results

Keyword filters captured over 4 372 293 LCC tweets. Analyses revealed that 17% of account users posting about LCCs were influencers and 1% of accounts were overtly commercial. Influencers were more likely to mention LCC brands and post promotional messages. Approximately 83% of LCC tweets contained references to marijuana and 29% of tweets were memes. Tweets also contained references to rap/hip-hop lyrics and urban subculture.

Conclusions

Twitter is a major information-sharing and marketing platform for LCCs. Co-use of tobacco and marijuana is common and normalised on Twitter. The presence and broad reach of LCC messages on social media warrants urgent need for surveillance and serious attention from public health professionals and policymakers. Future tobacco use prevention initiatives should be adapted to ensure that they are inclusive of LCC use.

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.

E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends

Background

As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs.

Objective

This study documents e-cigarette–related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health–related behaviors and attitudes.

Methods

E-cigarette–related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette–related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance.

Results

Clusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada).

Conclusions

Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace.

Using Social Media Data to Understand the Impact of Promotional Information on Laypeople's Discussions: A Case Study of Lynch Syndrome

Background

Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople.

Objective

This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople’s discussions on a social media platform (Twitter) in terms of topic awareness and attitudes.

Methods

We used topic modeling and sentiment analysis techniques on Lynch syndrome–related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome–related tweets?; (2) how promotional Lynch syndrome–related information on Twitter affects laypeople’s discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople’s discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome–related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople’s discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome–related tweets and conducted sentiment analysis on each layperson’s tweet to gauge the writer’s attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs.

Results

Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome–related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople’s discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople’s discussions were similar to the distributions in promotional Lynch syndrome–related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople’s discussions had a strong correlation with promotional Lynch syndrome–related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople’s discussions and their attitudes.

Conclusions

There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health.

Do you vape? Leveraging electronic health records to assess clinician documentation of electronic nicotine delivery system use among adolescents and adults

Use of electronic nicotine delivery systems (ENDS) has increased substantially over the past decade. However, unlike smoking, which is systematically captured by clinicians through routine screening and discrete documentation fields in the electronic health record (EHR), unknown is the extent to which clinicians are documenting patients' use of ENDS. Data were gathered from medical visits with patients aged 12 and older (N=9,119; 55% male) treated in a large, integrated healthcare system. We used natural language processing to assess the incidence rates of clinician documentation of patients' ENDS use in unstructured tobacco comments in the EHR, and the words most frequently documented in relation to ENDS, from 2006-2015. ENDS documentation in the EHR increased dramatically over time (from 0.01 to 9.5 per 10,000 patients, p<0.0001), particularly among adults aged 18-24 and 25-44. Most prevalent were "e-cig," "electronic cigarettes", and "vape," with much variation in spelling and phrasing of these words. Records of adolescent and young adult patients were more likely to contain the word "vape", and less likely to have "e-cig" and "electronic cigarette" than records of adults (ps<0.0001). The relatively low observed number of patients with ENDS terms in the EHR suggested vast under documentation. While healthcare providers are increasingly documenting patients' use of ENDS in the EHR, overall documentation rates remain low. Discrete EHR fields for standard screening and documentation of ENDS that reflect the language used by patients would provide more complete longitudinal population-level surveillance of ENDS use and its association with short- and long-term health outcomes.

Feasibility and reliability of a mobile tool to evaluate exposure to tobacco product marketing and messages using ecological momentary assessment

INTRODUCTION

Existing measures of tobacco marketing and messaging exposure are limited, relying on recall, recognition, or proxy measures. This study aimed to determine the feasibility and reliability of a mobile application for the measurement of tobacco and e-cigarette marketing and message exposure using ecological momentary assessment (EMA).

METHODS

Young adults from Austin, TX (n=181, ages 18-29) were instructed to use a mobile application to record all sightings of marketing or social media related to tobacco (including e-cigarettes) in real-time for 28days (Event EMAs). Tobacco product use and recall of message encounters were assessed daily using an app-initiated EMA (Daily EMAs).

RESULTS

The mobile app was a feasible and acceptable method to measure exposure to tobacco messages. The majority of messages (45.0%) were seen on the Internet, and many were user-generated. Thirty-day recall of messages at baseline was poorly correlated with messages reported via Event EMA during the study period; however, the correlation between post-study 30-day recall and Event EMA was much stronger (r=0.603 for industry-sponsored messages, r=0.599 for user-generated messages). Correlations between Daily EMAs and 30-day recall of message exposure (baseline and post-study) were small (baseline: r=0.329-0.389) to large (post-study: r=0.656-0.766).

CONCLUSIONS

These findings suggest that EMA is a feasible and reliable method for measuring tobacco message exposure, especially given the prevalence of messages encountered online and on social media. Recall measures are limited in their ability to accurately represent marketing exposure, but might be improved by a period of priming or clearer response categories.

Waterpipe Promotion and Use on Instagram: #Hookah

INTRODUCTION

Waterpipe (hookah) use is becoming more prevalent in the United States and abroad with potential implications for public health. As waterpipe use rapidly grows in popularity, novel data streams are needed that can help capture and document the social and environmental context in which individuals use, and are marketed, this emerging tobacco product. This study characterized waterpipe-related posts on Instagram in order to inform regulatory and policy activities in the United States.

METHODS

Data were collected from Instagram, an image-based social media site. Inclusion criteria for this study comprised an Instagram post with the hashtag "#hookah" that was accompanied by geo-location metadata demonstrating that the post was inside the contiguous United States. Rules were established for coding themes of images (n = 1705).

RESULTS

Seven percent of images depicted a single person using a waterpipe and/or blowing smoke, 25% depicted two or more persons lounging and/or using a waterpipe, 6% depicted waterpipes, coals or flavored tobacco without people, 18% of images were promotional material for hookah lounges and restaurant/bars/nightclubs referencing hookah in the text or depicting a waterpipe, 25% were non-waterpipe-related promotional material, 1% were sexually explicit material, and 18% other. 31% of all images depicted or referenced alcohol. 30% of posts provided geo-location from a hookah lounge, 56% from a restaurant/bar/nightclub, and 14% from other types of locations.

CONCLUSIONS

The cross promotion of waterpipe and alcohol use by hookah lounges, and restaurants/bars/nightclubs suggests that poly-substance use is regularly depicted, and promoted, in nightlife entertainment as well as normalized on Instagram in the United States.

IMPLICATIONS

In the US Instagram posts with the hashtag #hookah regularly depicted waterpipe use in conjunction with alcohol use. Instagram's focus on images facilitates picture-based advertising where hookah lounges promote drink specials at the same time nightclubs promote waterpipe specials. Instagram users often document the variety of shapes, sizes, and styles, of waterpipes, suggesting users take pride in their purchases and like to show their customized, and often times elaborate, waterpipes to their followers. Instagram has been underutilized in understanding tobacco related-behaviors and identifying tobacco-related promotional material.

Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15

This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009-2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population.

Exposure and Engagement With Tobacco- and E-Cigarette-Related Social Media

PURPOSE

Little is known about the nature and extent of adolescents' exposure to tobacco- and e-cigarette-related communications on social media. In this study, we describe the prevalence and correlates of youth exposure and engagement with tobacco- and e-cigarette-related social media.

METHODS

Data are from the baseline survey of the Texas Adolescent Tobacco and Marketing Surveillance system, a cross-sectional sample of sixth, eighth, and 10th graders (n = 3907, N = 461,097). Weighted logistic regression models were used to examine associations between demographic characteristics, sensation seeking, tobacco use, and exposure and engagement with tobacco-related social media.

RESULTS

Overall, 52.5% of students reported exposure to tobacco-related social media in the past month, whereas < 6% reported engagement. Exposure and some forms of engagement were more common among high school students, girls, those with friends who use tobacco, and high sensation seekers (p < .05). The odds of exposure were significantly higher among students susceptible to combustible tobacco (adjusted odds ratio [AOR] = 1.71, p < .05), e-cigarettes (AOR = 2.10, p < .01), and both combustible tobacco and e-cigarettes (AOR = 2.24, p < .001). The odds of engaging with social media was higher among those who were susceptible to, had ever, or currently use both combustible tobacco and e-cigarettes (AOR = 2.10-3.46, p < .05).

CONCLUSIONS

About 1 in every 2 adolescents in Texas are exposed to tobacco-related social media. Adolescents who are susceptible to or use e-cigarettes and/or combustible tobacco are exposed to and engage with tobacco-related social media more than their peers. Social media appears to be an important venue when targeting vulnerable youth in prevention campaigns.

A picture is worth a thousand words: Electronic cigarette content on Instagram and Pinterest

INTRODUCTION

This study examined electronic cigarette (e-cig) content in visual materials posted on the social-media platforms Instagram and Pinterest. Both platforms allow users to upload pictures to the internet and share them globally. Users can search for pictures tagged with specific keywords and phrases.

METHODS

Using content analysis, this study identified themes in image postings of e-cigs on social media. During five weeks of data collection, keywords were used to identify pictures related to e-cigs. These pictures were then coded into one or more categories.

RESULTS

The three most popular categories for Instagram posts were marketing, customization and juices/flavors. The three most popular categories for Pinterest posts were customization, marketing and memes.

CONCLUSIONS

Because of the persuasive power of visuals, it is important to examine communication on Instagram and Pinterest as well as the specific visual messages communicated. Stores and manufacturers use these and similar platforms to communicate with users and potential users; thus it seems that marketers are capitalizing on opportunities for persuasive appeal. The results highlight the popularity of e-cig content on these two social media platforms and reveal an emphasis on marketing and customization.

A picture is worth a thousand words: Electronic cigarette content on Instagram and Pinterest

INTRODUCTION

This study examined electronic cigarette (e-cig) content in visual materials posted on the social-media platforms Instagram and Pinterest. Both platforms allow users to upload pictures to the internet and share them globally. Users can search for pictures tagged with specific keywords and phrases.

METHODS

Using content analysis, this study identified themes in image postings of e-cigs on social media. During five weeks of data collection, keywords were used to identify pictures related to e-cigs. These pictures were then coded into one or more categories.

RESULTS

The three most popular categories for Instagram posts were marketing, customization and juices/flavors. The three most popular categories for Pinterest posts were customization, marketing and memes.

CONCLUSIONS

Because of the persuasive power of visuals, it is important to examine communication on Instagram and Pinterest as well as the specific visual messages communicated. Stores and manufacturers use these and similar platforms to communicate with users and potential users; thus it seems that marketers are capitalizing on opportunities for persuasive appeal. The results highlight the popularity of e-cig content on these two social media platforms and reveal an emphasis on marketing and customization.

Public Response to Obamacare on Twitter

Background

The Affordable Care Act (ACA), often called “Obamacare,” is a controversial law that has been implemented gradually since its enactment in 2010. Polls have consistently shown that public opinion of the ACA is quite negative.

Objective

The aim of our study was to examine the extent to which Twitter data can be used to measure public opinion of the ACA over time.

Methods

We prospectively collected a 10% random sample of daily tweets (approximately 52 million since July 2011) using Twitter’s streaming application programming interface (API) from July 10, 2011 to July 31, 2015. Using a list of key terms and ACA-specific hashtags, we identified tweets about the ACA and examined the overall volume of tweets about the ACA in relation to key ACA events. We applied standard text sentiment analysis to assign each ACA tweet a measure of positivity or negativity and compared overall sentiment from Twitter with results from the Kaiser Family Foundation health tracking poll.

Results

Public opinion on Twitter (measured via sentiment analysis) was slightly more favorable than public opinion measured by the Kaiser poll (approximately 50% vs 40%, respectively) but trends over time in both favorable and unfavorable views were similar in both sources. The Twitter-based measures of opinion as well as the Kaiser poll changed very little over time: correlation coefficients for favorable and unfavorable public opinion were .43 and .37, respectively. However, we found substantial spikes in the volume of ACA-related tweets in response to key events in the law’s implementation, such as the first open enrollment period in October 2013 and the Supreme Court decision in June 2012.

Conclusions

Twitter may be useful for tracking public opinion of health care reform as it appears to be comparable with conventional polling results. Moreover, in contrast with conventional polling, the overall amount of tweets also provides a potential indication of public interest of a particular issue at any point in time.

Research on Skin Cancer-Related Behaviors and Outcomes in the NIH Grant Portfolio, 2000-2014: Skin Cancer Intervention Across the Cancer Control Continuum (SCI-3C)

Importance

The Surgeon General's Call to Action to Prevent Skin Cancer broadly identified research gaps, but specific objectives are needed to further behavioral intervention research.

Objective

To review National Institute of Health (NIH) grants targeting skin cancer-related behaviors and relevant outcomes.

Design, Setting, and Participants

A portfolio analysis of the title, abstract, specific aims, and research plans of identified grant applications from 2000 to 2014 targeting skin cancer-related behaviors or testing behavioral intervention effects on cancer-relevant outcomes along the cancer continuum.

Main Outcomes and Measures

Funding trends were compared along the cancer control continuum, with respect to investigator demographics and use of theory, technology, policy, and changes to environmental surroundings (built environment).

Results

A total of 112 submitted applications met inclusion criteria; of these, 40 (35.7%) were funded, and 31 of the 40 were interventions. Comparing the 40 funded grants with the 72 unfunded grants, the overall success rates did not differ significantly between male (33.3%) and female (37.3%) investigators, nor did the frequency of R01 awards (36.7% and 28.1%, respectively). Among intervention awards, most (24 of 31) addressed prevention. Fewer awards targeted detection alone or in conjunction with prevention (3) or cancer survivorship (4), and no grant addressed emotional sequelae or adherence behavior related to diagnosis or treatment. Fewer than half of funded grants aimed for clinically related targets (eg, sunburn reduction). Use of theory and technology occurred in more than 75% of grants. However, the full capability of proposed technology was infrequently used, and rarely did constructs of the proposed behavior change theory clearly and comprehensively drive the intervention approach. Policy or environmental manipulation was present in all dissemination grants but was rarely used elsewhere, and 19.4% included policy implementation and 25.8% proposed changes in built environment.

Conclusions and Relevance

Grant success rate in skin cancer-related behavioral science compares favorably to the overall NIH grant success rate (approximately 18%), and the success rate of male and female investigators was not statistically different. However, gaps exist in behavioral research addressing all points of the skin cancer control continuum, measuring interventions that hit clinically related targets, and leveraging technology, theory, and environmental manipulation to optimize intervention approach.

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.

Reactions on Twitter to updated alcohol guidelines in the UK: a content analysis

OBJECTIVES

In January 2016, the 4 UK Chief Medical Officers released a public consultation regarding updated guidelines for low-risk alcohol consumption. This study aimed to assess responses to the updated guidelines using comments made on Twitter.

METHODS

Tweets containing the hashtag #alcoholguidelines made during 1 week following the announcement of the updated guidelines were retrieved using the Twitter Archiver tool. The source, sentiment and themes of the tweets were categorised using manual content analysis.

RESULTS

A total of 3061 tweets was retrieved. 6 sources were identified, the most prominent being members of the public. Of 821 tweets expressing sentiment specifically towards the guidelines, 80% expressed a negative sentiment. 11 themes were identified, 3 of which were broadly supportive of the guidelines, 7 broadly unsupportive and 1 neutral. Overall, more tweets were unsupportive (49%) than supportive (44%). While the most common theme overall was sharing information, the most common in tweets from members of the public encouraged alcohol consumption (15%) or expressed disagreement with the guidelines (14%), reflecting reactance, resistance and misunderstanding.

CONCLUSIONS

This descriptive analysis revealed a number of themes present in unsupportive comments towards the updated UK alcohol guidelines among a largely proalcohol community. An understanding of these may help to tailor effective communication of alcohol and health-related policies, and could inform a more dynamic approach to health communication via social media.

Perceptions of Menthol Cigarettes Among Twitter Users: Content and Sentiment Analysis

Background

Menthol cigarettes are used disproportionately by African American, female, and adolescent smokers. Twitter is also used disproportionately by minority and younger populations, providing a unique window into conversations reflecting social norms, behavioral intentions, and sentiment toward menthol cigarettes.

Objective

Our purpose was to identify the content and frequency of conversations about menthol cigarettes, including themes, populations, user smoking status, other tobacco or substances, tweet characteristics, and sentiment. We also examined differences in menthol cigarette sentiment by prevalent categories, which allowed us to assess potential perceptions, misperceptions, and social norms about menthol cigarettes on Twitter. This approach can inform communication about these products, particularly to subgroups who are at risk for menthol cigarette use.

Methods

Through a combination of human and machine classification, we identified 94,627 menthol cigarette-relevant tweets from February 1, 2012 to January 31, 2013 (1 year) from over 47 million tobacco-related messages gathered prospectively from the Twitter Firehose of all public tweets and metadata. Then, 4 human coders evaluated a random sample of 7000 tweets for categories, including sentiment toward menthol cigarettes.

Results

We found that 47.98% (3194/6657) of tweets expressed positive sentiment, while 40.26% (2680/6657) were negative toward menthol cigarettes. The majority of tweets by likely smokers (2653/4038, 65.70%) expressed positive sentiment, while 91.2% (320/351) of nonsmokers and 71.7% (91/127) of former smokers indicated negative views. Positive views toward menthol cigarettes were predominant in tweets that discussed addiction or craving, marijuana, smoking, taste or sensation, song lyrics, and tobacco industry or marketing or tweets that were commercial in nature. Negative views toward menthol were more common in tweets about smoking cessation, health, African Americans, women, and children and adolescents—largely due to expression of negative stereotypes associated with these groups’ use of menthol cigarettes.

Conclusions

Examinations of public opinions toward menthol cigarettes through social media can help to inform the framing of public communication about menthol cigarettes, particularly in light of potential regulation by the European Union, US Food and Drug Administration, other jurisdictions, and localities.

Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis

Background

The nonmedical use of pharmaceutical products has become a significant public health concern. Traditionally, the evaluation of nonmedical use has focused on controlled substances with addiction risk. Currently, there is no effective means of evaluating the nonmedical use of noncontrolled antidepressants.

Objective

Social listening, in the context of public health sometimes called infodemiology or infoveillance, is the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media. The objectives of this study were (1) to determine whether content analysis of social listening data could be utilized to identify posts discussing potential misuse or nonmedical use of bupropion and two comparators, amitriptyline and venlafaxine, and (2) to describe and characterize these posts.

Methods

Social listening was performed on all publicly available posts cumulative through July 29, 2015, from two harm-reduction Web forums, Bluelight and Opiophile, which mentioned the study drugs. The acquired data were stripped of personally identifiable identification (PII). A set of generic, brand, and vernacular product names was used to identify product references in posts. Posts were obtained using natural language processing tools to identify vernacular references to drug misuse-related Preferred Terms from the English Medical Dictionary for Regulatory Activities (MedDRA) version 18 terminology. Posts were reviewed manually by coders, who extracted relevant details.

Results

A total of 7756 references to at least one of the study antidepressants were identified within posts gathered for this study. Of these posts, 668 (8.61%, 668/7756) referenced misuse or nonmedical use of the drug, with bupropion accounting for 438 (65.6%, 438/668). Of the 668 posts, nonmedical use was discouraged by 40.6% (178/438), 22% (22/100), and 18.5% (24/130) and encouraged by 12.3% (54/438), 10% (10/100), and 10.8% (14/130) for bupropion, amitriptyline, and venlafaxine, respectively. The most commonly reported desired effects were similar to stimulants with bupropion, sedatives with amitriptyline, and dissociatives with venlafaxine. The nasal route of administration was most frequently reported for bupropion, whereas the oral route was most frequently reported for amitriptyline and venlafaxine. Bupropion and venlafaxine were most commonly procured from health care providers, whereas amitriptyline was most commonly obtained or stolen from a third party. The Fleiss kappa for interrater agreement among 20 items with 7 categorical response options evaluated by all 11 raters was 0.448 (95% CI 0.421-0.457).

Conclusions

Social listening, conducted in collaboration with harm-reduction Web forums, offers a valuable new data source that can be used for monitoring nonmedical use of antidepressants. Additional work on the capabilities of social listening will help further delineate the benefits and limitations of this rapidly evolving data source.

How US Smokers Refer to E-cigarettes: An Examination of User-Generated Posts From a Web-Based Smoking Cessation Intervention, 2008-2015

INTRODUCTION

A challenge in Electronic Nicotine Delivery System (ENDS) research is how to refer to these devices in ways that are meaningful to current or potential users. The objectives of this study were to: (1) describe the frequency of ENDS terms in a web-based smoking cessation intervention; and (2) determine whether terms vary by US geographic region and date.

METHODS

Data were drawn from public posts between 2008-2015 on http://BecomeAnEX.org and limited to US users. We conducted "exact" and "fuzzy" searches to find posts containing ENDS keywords using custom Python scripts, and extracted geocoding data and date for each post. We examined counts and frequencies of ENDS terms by unique user, by unique user and region, and by unique user and date.

RESULTS

We identified 1023 unique US website users who had written a post containing one or more ENDS keywords. Posters were majority female (79%), educated (78% attended at least some college), and had a median age of 47 years. Overall, 92% of ENDS posters employed the term "e-cigarette" or a derivation. Derivations of "vape" became increasingly popular in 2013, whereas "NJoy" and "blu" were employed by fewer than 2% of posters. We found no variation in frequency of ENDS terms by US region.

CONCLUSIONS

Researchers may have confidence that "e-cigarette" and "vape" are recognizable terms among US treatment-seeking smokers. Conversely, terms such as "ENDS," commonly employed by researchers and public health advocates, are not used by smokers and may be an impediment to tobacco control research.

IMPLICATIONS

Researchers may have confidence that "e-cigarette," and, to a lesser extent, "vape" are recognizable terms among US adult smokers referring to ENDS (including accessories, brand names, and actions). Conversely, terms such as "electronic nicotine delivery systems," commonly employed by researchers and public health advocates, are not used by US smokers and may be an impediment to tobacco control research and practice.

Overview of Electronic Nicotine Delivery Systems: A Systematic Review

CONTEXT

Rapid developments in e-cigarettes, or electronic nicotine delivery systems (ENDS), and the evolution of the overall tobacco product marketplace warrant frequent evaluation of the published literature. The purpose of this article is to report updated findings from a comprehensive review of the published scientific literature on ENDS.

EVIDENCE ACQUISITION

The authors conducted a systematic review of published empirical research literature on ENDS through May 31, 2016, using a detailed search strategy in the PubMed electronic database, expert review, and additional targeted searches. Included studies presented empirical findings and were coded to at least one of nine topics: (1) Product Features; (2) Health Effects; (3) Consumer Perceptions; (4) Patterns of Use; (5) Potential to Induce Dependence; (6) Smoking Cessation; (7) Marketing and Communication; (8) Sales; and (9) Policies; reviews and commentaries were excluded. Data from included studies were extracted by multiple coders (October 2015 to August 2016) into a standardized form and synthesized qualitatively by topic.

EVIDENCE SYNTHESIS

There were 687 articles included in this systematic review. The majority of studies assessed patterns of ENDS use and consumer perceptions of ENDS, followed by studies examining health effects of vaping and product features.

CONCLUSIONS

Studies indicate that ENDS are increasing in use, particularly among current smokers, pose substantially less harm to smokers than cigarettes, are being used to reduce/quit smoking, and are widely available. More longitudinal studies and controlled trials are needed to evaluate the impact of ENDS on population-level tobacco use and determine the health effects of longer-term vaping.

Recent Themes in Social Networking Service Research

The body of literature addressing the phenomenon related to social networking services (SNSs) has grown rather fast recently. Through a systematic and quantitative approach, this study identifies the recent SNS research themes, which are the issues discussed by a coherent and growing subset of this literature. A set of academic articles retrieved from the Web of Science database is used as the basis for uncovering the recent themes. We begin the analysis by constructing a citation network which is further separated into groups after applying a widely used clustering method. The resulting clusters all consist of articles coherent in citation relationships. This study suggests eight fast growing recent themes. They span widely encompassing politics, romantic relationships, public relations, journalism, and health. Among them, four focus their issues largely on Twitter, three on Facebook, and one generally on both. While discussions on traditional issues in SNSs such as personality, motivations, self-disclosure, narcissism, etc. continue to lead the pack, the proliferation of the highlighted recent themes in the near future is very likely to happen.

Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms

Background

Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms.

Objective

This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies.

Methods

We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 “e-cig ban”-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data.

Results

We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms.

Conclusions

This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media.

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.

Chiropractic and Spinal Manipulation Therapy on Twitter: Case Study Examining the Presence of Critiques and Debates

Background

Spinal manipulation therapy (SMT) is a popular though controversial practice. The debates surrounding efficacy and risk of SMT are only partially evident in popular discourse.

Objective

This study aims to investigate the presence of critiques and debates surrounding efficacy and risk of SMT on the social media platform Twitter. The study examines whether there is presence of debate and whether critical information is being widely disseminated.

Methods

An initial corpus of 31,339 tweets was compiled through Twitter’s Search Application Programming Interface using the query terms “chiropractic,” “chiropractor,” and “spinal manipulation therapy.” Tweets were collected for the month of December 2015. Post removal of tweets made by bots and spam, the corpus totaled 20,695 tweets, of which a sample (n=1267) was analyzed for skeptical or critical tweets. Additional criteria were also assessed.

Results

There were 34 tweets explicitly containing skepticism or critique of SMT, representing 2.68% of the sample (n=1267). As such, there is a presence of 2.68% of tweets in the total corpus, 95% CI 0-6.58% displaying explicitly skeptical or critical perspectives of SMT. In addition, there are numerous tweets highlighting the health benefits of SMT for health issues such as attention deficit hyperactivity disorder (ADHD), immune system, and blood pressure that receive scant critical attention. The presence of tweets in the corpus highlighting the risks of “stroke” and “vertebral artery dissection” is also minute (0.1%).

Conclusions

In the abundance of tweets substantiating and promoting chiropractic and SMT as sound health practices and valuable business endeavors, the debates surrounding the efficacy and risks of SMT on Twitter are almost completely absent. Although there are some critical voices of SMT proving to be influential, issues persist regarding how widely this information is being disseminated.

A content analysis of tweets about high-potency marijuana

INTRODUCTION

"Dabbing" involves heating extremely concentrated forms of marijuana to high temperatures and inhaling the resulting vapor. We studied themes describing the consequences of using highly concentrated marijuana by examining the dabbing-related content on Twitter.

METHODS

Tweets containing dabbing-related keywords were collected from 1/1-1/31/2015 (n=206,854). A random sample of 5000 tweets was coded for content according to pre-determined categories about dabbing-related behaviors and effects experienced using a crowdsourcing service. An examination of tweets from the full sample about respiratory effects and passing out was then conducted by selecting tweets with relevant keywords.

RESULTS

Among the 5000 randomly sampled tweets, 3540 (71%) were related to dabbing marijuana concentrates. The most common themes included mentioning current use of concentrates (n=849; 24%), the intense high and/or extreme effects from dabbing (n=763; 22%) and excessive/heavy dabbing (n=517; 15%). Extreme effects included both physiological (n=124/333; 37%) and psychological effects (n=55/333; 17%). The most common physiologic effects, passing out (n=46/333; 14%) and respiratory effects (n=30/333; 9%), were then further studied in the full sample of tweets. Coughing was the most common respiratory effect mentioned (n=807/1179; 68%), and tweeters commonly expressed dabbing with intentions to pass out (416/915; 45%).

CONCLUSIONS

This study adds to the limited understanding of marijuana concentrates and highlights self-reported physical and psychological effects from this type of marijuana use. Future research should further examine these effects and the potential severity of health consequences associated with concentrates.

A pattern-matched Twitter analysis of US cancer-patient sentiments

BACKGROUND

Twitter has been recognized as an important source of organic sentiment and opinion. This study aimed to (1) characterize the content of tweets authored by the United States cancer patients; and (2) use patient tweets to compute the average happiness of cancer patients for each cancer diagnosis.

METHODS

A large sample of English tweets from March 2014 through December 2014 was obtained from Twitter. Using regular expression software pattern matching, the tweets were filtered by cancer diagnosis. For each cancer-specific tweetset, individual patients were extracted, and the content of the tweet was categorized. The patients' Twitter identification numbers were used to gather all tweets for each patient, and happiness values for patient tweets were calculated using a quantitative hedonometric analysis.

RESULTS

The most frequently tweeted cancers were breast (n = 15,421, 11% of total cancer tweets), lung (n = 2928, 2.0%), prostate (n = 1036, 0.7%), and colorectal (n = 773, 0.5%). Patient tweets pertained to the treatment course (n = 73, 26%), diagnosis (n = 65, 23%), and then surgery and/or biopsy (n = 42, 15%). Computed happiness values for each cancer diagnosis revealed higher average happiness values for thyroid (h_avg = 6.1625), breast (h_avg = 6.1485), and lymphoma (h_avg = 6.0977) cancers and lower average happiness values for pancreatic (h_avg = 5.8766), lung (h_avg = 5.8733), and kidney (h_avg = 5.8464) cancers.

CONCLUSIONS

The study confirms that patients are expressing themselves openly on social media about their illness and that unique cancer diagnoses are correlated with varying degrees of happiness. Twitter can be employed as a tool to identify patient needs and as a means to gauge the cancer patient experience.

Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection

Background

Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies.

Objective

We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies.

Methods

We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on—accurate human coding and full data collection—and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose.

Results

We developed and applied a search filter to retrieve e-cigarette–related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette–related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders.

Conclusions

This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.