Identifying Sentiment of Hookah-Related Posts on Twitter

Spanish-Language Tobacco-Related Posts on Twitter: Content Analysis

INTRODUCTION

Twitter data have been used to surveil public sentiment about tobacco products; however, most tobacco-related Twitter research has been conducted with English-language posts. There is a gap in the literature on tobacco-related discussions on Twitter in languages other than English. This study summarized tobacco-related discussions in Spanish on Twitter.

METHODS

A set of Spanish terms reflecting electronic cigarettes (eg, "cigarillos electrónicos"), cigarettes (eg, "pitillo"), and cigars (eg, "cigaro") were identified. A content analysis of tweets (n = 1352) drawn from 2021 was performed to examine themes and sentiment. An initial codebook was developed in English then translated to Spanish and then translated back to English by a bilingual (Spanish and English) member of the research team. Two bilingual members of the research team coded the tweets into themes and sentiment.

RESULTS

Themes in the tweets included (1) product promotion (n = 168, 12.4%), (2) health warnings (n = 161, 11.9%), (3) tobacco use (n = 136, 10.1%), (4) health benefits of vaping (n = 58, 4.3%), (5) cannabis use (n = 50, 3.7%), (6) cessation (n = 47, 3.5%), (7) addiction (n = 33, 2.4%), (8) policy (n = 27, 2.0%), and (9) polysubstance use (n = 12, 0.9%). Neutral (n = 955, 70.6%) was the most common category of sentiment observed in the data.

CONCLUSIONS

Tobacco products are discussed in multiple languages on Twitter and can be summarized by bilingual research teams. Future research should determine if Spanish-speaking individuals are frequently exposed to pro-tobacco content on social media and if such exposure increases susceptibility to use tobacco among never users or sustained use among current users.

IMPLICATIONS

Spanish-language pro-tobacco content exists on Twitter, which has implications for Spanish-speaking individuals who may be exposed to this content. Spanish-language pro-tobacco-related posts may help normalize tobacco use among Spanish-speaking populations. As a result, anti-tobacco tweets in Spanish may be necessary to counter areas of the online environment that can be considered pro-tobacco.

Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis

BACKGROUND

On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored.

OBJECTIVE

This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law.

METHODS

We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times.

RESULTS

The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation.

CONCLUSIONS

Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

AIMS

There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms.

METHODS

This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included.

RESULTS

A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified.

CONCLUSIONS

Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.

Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study

Background

Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences.

Objective

In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data.

Methods

We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as "waterpipe," "hookah," and "shisha." We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe-related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe-related posts before and during the pandemic.

Results

A total of 45,765 nonpromotion water pipe-related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word "home" remained similar during the COVID-19 pandemic (P=.29). While water pipe-related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe-related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic.

Conclusions

This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit.

The Longitudinal Impact of Seeing and Posting Tobacco-related Social Media on Tobacco Use Behaviors Among Youth (Aged 12-17): Findings From the 2014-2016 Population Assessment of Tobacco and Health (PATH) Study

Introduction

This study examined the impact of seeing and posting tobacco-related content on social media on tobacco use outcomes in youth.

Methods

Longitudinal secondary analyses of youth in the nationally representative 2014-2015 Population Assessment of Tobacco and Health (PATH) study were conducted to examine the association between the interaction of (i) seeing and (ii) posting tobacco-related social media content with previous ever use of each tobacco product, and 3 outcomes in 2015-2016: past 30-day e-cigarette use, past 30-day combustible product use, and past 30-day dual use of e-cigarettes and at least one combustible product. Six weighted multiple logistic regression models (2 interaction exposures*3 outcomes) were used to assess these associations, while adjusting for covariates.

Results

Among youth never users in 2014-2015, seeing tobacco-related social media content was significantly associated with past 30-day e-cigarette use (AOR 1.92; 95% CI= 1.36-2.71), and past 30-day dual use of e-cigarettes and at least one combustible product (AOR= 2.11; 95% CI= 1.08- 4.13) in 2015-2016. Among youth ever users in 2014-2015, posting tobacco-related content on social media was significantly associated with all 3 outcomes: past 30-day day e-cigarette use (AOR= 2.09;95%CI=1.23-3.55), past 30-day combustible product use (AOR=2.86; 95%CI=1.67-4.88), and past 30-day dual use of these products (AOR=3.02;95%CI=1.45-6.31), after adjusting for covariates.

Conclusions

Seeing and posting tobacco-related content on social media predicts tobacco use among youth, nationwide. Results suggest that interventions and policies prohibiting tobacco-related content on social media are needed to curb the impact of social media on youth tobacco-use.

General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis

BACKGROUND

Public health organizations have begun to use social media to increase awareness of health harm and positively improve health behavior. Little is known about effective strategies to disseminate health education messages digitally and ultimately achieve optimal audience engagement.

OBJECTIVE

This study aims to assess the difference in audience engagement with identical antismoking health messages on three social media sites (Twitter, Facebook, and Instagram) and with a referring link to a tobacco prevention website cited in these messages. We hypothesized that health messages might not receive the same user engagement on these media, although these messages were identical and distributed at the same time.

METHODS

We measured the effect of health promotion messages on the risk of smoking among users of three social media sites (Twitter, Facebook, and Instagram) and disseminated 1275 health messages between April 19 and July 12, 2017 (85 days). The identical messages were distributed at the same time and as organic (unpaid) and advertised (paid) messages, each including a link to an educational website with more information about the topic. Outcome measures included message engagement (ie, the click-through rate [CTR] of the social media messages) and educational website engagement (ie, the CTR on the educational website [wCTR]). To analyze the data and model relationships, we used mixed effects negative binomial regression, z-statistic, and the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

Comparisons between social media sites showed that CTRs for identical antitobacco health messages differed significantly across social media (P<.001 for all). Instagram showed the statistically significant highest overall mean message engagement (CTR=0.0037; 95% CI 0.0032-0.0042), followed by Facebook (CTR=0.0026; 95% CI 0.0022-0.0030) and Twitter (CTR=0.0015; 95% CI 0.0013-0.0017). Facebook showed the highest as well as the lowest CTR for any individual message. However, the message CTR is not indicative of user engagement with the educational website content. Pairwise comparisons of the social media sites differed with respect to the wCTR (P<.001 for all). Messages on Twitter showed the lowest CTR, but they resulted in the highest level of website engagement (wCTR=0.6308; 95% CI 0.5640-0.6975), followed by Facebook (wCTR=0.2213; 95% CI 0.1932-0.2495) and Instagram (wCTR=0.0334; 95% CI 0.0230-0.0438). We found a statistically significant higher CTR for organic (unpaid) messages (CTR=0.0074; 95% CI 0.0047-0.0100) compared with paid advertisements (CTR=0.0022; 95% CI 0.0017-0.0027; P<.001 and P<.001, respectively).

CONCLUSIONS

Our study provides evidence-based insights to guide the design of health promotion efforts on social media. Future studies should examine the platform-specific impact of psycholinguistic message variations on user engagement, include newer sites such as Snapchat and TikTok, and study the correlation between web-based behavior and real-world health behavior change. The need is urgent in light of increased health-related marketing and misinformation on social media.

Prevalence of Health Misinformation on Social Media: Systematic Review

Background

Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated.

Objective

This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern.

Methods

We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms.

Results

A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%).

Conclusions

The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours

OBJECTIVE

Social media analytics (SMA) has a track record in business research. The utilisation in nutrition research is unknown, despite social media being populated with real-time eating behaviours. This rapid review aimed to explore the use of SMA in nutrition research with the investigation of dietary behaviours.

DESIGN

The review was conducted according to rapid review guidelines by WHO and the National Collaborating Centre for Methods and Tools. Five databases of peer-reviewed, English language studies were searched using the keywords 'social media' in combination with 'data analytics' and 'food' or 'nutrition' and screened for those with general population health using SMA on public domain, social media data between 2014 and 2020.

RESULTS

The review identified 34 studies involving SMA in the investigation of dietary behaviours. Nutrition topics included population nutrition health investigations, alcohol consumption, dieting and eating out of the home behaviours. All studies involved content analysis with evidence of surveillance and engagement. Twitter was predominant with data sets in tens of millions. SMA tools were observed in data discovery, collection and preparation, but less so in data analysis. Approximately, a third of the studies involved interdisciplinary collaborations with health representation and only two studies involved nutrition disciplines. Less than a quarter of studies obtained formal human ethics approval.

CONCLUSIONS

SMA in nutrition research with the investigation of dietary behaviours is emerging, nevertheless, if consideration is taken with technological capabilities and ethical integrity, the future shows promise at a broad population census level and as a scoping tool or complementary, triangulation instrument.

Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis

Background

Prior research suggests that social media–based public health campaigns are often targeted by countercampaigns.

Objective

Using reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media–based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion.

Methods

User comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions.

Results

Of the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75%) and disagreed with the health messages (39.77%), while the majority made healthy contributions to the discussions (84.38%). Only 0.56% of messages mentioned government agencies, and only 0.48% of the comments were antiregulation. Comments employing a positive tone (84.13%) or making healthy contributions (69.11%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25%) or making toxic contributions (36.26%) generally disagreed with the messages (P=0.01).

Conclusions

The majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media–based tobacco prevention campaigns to consider.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Infodemiology and Infoveillance: Scoping Review

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Combining Real-Time Ratings With Qualitative Interviews to Develop a Smoking Cessation Text Messaging Program for Primary Care Patients

Background

Text messaging (short message service, SMS) interventions show promise as a way to help cigarette smokers quit. Few studies have examined the effectiveness of text messaging (SMS) programs targeting smokers associated with primary care or hospital settings.

Objective

This study aimed to develop a text messaging (SMS) program targeting primary care smokers.

Methods

Adult smokers in primary care were recruited from February 2017 to April 2017. We sent patients 10 to 11 draft text messages (SMS) over 2 days and asked them to rate each message in real time. Patients were interviewed daily by telephone to discuss ratings, message preferences, and previous experiences with nicotine replacement therapy (NRT). Content analysis of interviews was directed by a step-wise text messaging (SMS) intervention development process and the Information-Motivation-Behavioral Skills model of medication adherence.

Results

We sent 149 text messages (SMS) to 15 patients. They replied with ratings for 93% (139/149) of the messages: 134 (96%, 134/139) were rated as clear or useful and 5 (4%, 5/139) as unclear or not useful. Patients’ preferences included the addition of graphics, electronic cigarette (e-cigarette) content, and use of first names. Regarding NRT, patients identified informational gaps around safety and effectiveness, preferred positively framed motivational messages, and needed behavioral skills to dose and dispose of NRT.

Conclusions

Patients recommended text message (SMS) personalization, inclusion of e-cigarette information and graphics, and identified barriers to NRT use.

Combining real-time ratings with telephone interviews is a feasible method for incorporating primary care patients’ preferences into a behavioral text messaging (SMS) program.

How Do Waterpipe Smoking Establishments Attract Smokers? Implications for Policy

BACKGROUND

Given the unique social nature of waterpipe smoking, an important factor contributing to its popularity is the spread of waterpipe establishments.

OBJECTIVES

With a focus on implications for regulations, we conducted a qualitative assessment of customers' online reviews on Yelp.com to gain insight into their positive and negative perceptions about waterpipe establishments and products, and identify features that are most important to them.

METHODS

In June 2016, an online search of Yelp was conducted to identify waterpipe establishments in Miami, Florida. First, we collected information from the websites on establishments' characteristics and their marketing practices. Then we selected customers' waterpipe-related reviews and used an inductive qualitative method to code and identify key themes associated with positive and negative customers' experiences. Thematic analysis was completed upon reaching saturation. The final coding scheme consisted of 32 codes within eight themes.

RESULTS

The homepage of the establishment was used to promote special discounts and events, while the online waterpipe menu was used to promote the waterpipe products. Our thematic analysis indicated that the variety of flavored tobacco was the most rated positive factor to customers, while the low-quality charcoal and high price were the most negative factors. Conclusions/Importance: Waterpipe online advertisements and promotions should be monitored and restricted. The availability of flavored tobacco, innovative device/accessories, affordable pricing, and charcoal quality are important domains for waterpipe establishments policy/regulation. Regulatory framework for waterpipe establishments should address the complex context of waterpipe including the venue (i.e., physical, website, menu), the tobacco, the device/accessories, and charcoal.

Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification

Background

Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images.

Objective

This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image.

Methods

Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy.

Results

As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone.

Conclusions

A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies.

Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis

Background

Hookah (or tobacco waterpipe) use has recently become prevalent in the United States. The contexts and experiences associated with hookah use are unclear, yet such information is abundant via publicly available hookah users’ social media postings.

Objective

In this study, we utilized Twitter data to characterize Twitter users’ recent experiences with hookah.

Methods

Twitter posts containing the term “hookah” were obtained from April 1, 2017 to 29 March, 2018. Text classifiers were used to identify clusters of topics that tended to co-occur in posts (n=176,706).

Results

The most prevalent topic cluster was Person Tagging (use of @username to tag another Twitter account in a post) at 21.58% (38,137/176,706) followed by Promotional or Social Events (eg, mentions of ladies’ nights, parties, etc) at 20.20% (35,701/176,706) and Appeal or Abuse Liability (eg, craving, enjoying hookah) at 18.12% (32,013/176,706). Additional topics included Hookah Use Behavior (eg, mentions of taking a “hit” of hookah) at 11.67% (20,603/176,706), Polysubstance Use (eg, hookah use along with other substances) at 10.95% (19,353/176,706), Buying or Selling (eg, buy, order, purchase, sell) at 9.37% (16,552/176,706), and Flavors (eg, mint, cinnamon, watermelon) at 1.66% (2927/176,706). The topic Dislike of Hookah (eg, hate, quit, dislike) was rare at 0.59% (1043/176,706).

Conclusions

Social events, appeal or abuse liability, flavors, and polysubstance use were the common contexts and experiences associated with Twitter discussions about hookah in 2017-2018. Considered in concert with traditional data sources about hookah, these results suggest that social events, appeal or abuse liability, flavors, and polysubstance use warrant consideration as targets in future surveillance, policy making, and interventions addressing hookah.

Next generation media monitoring: Global coverage of electronic nicotine delivery systems (electronic cigarettes) on Bing, Google and Twitter, 2013-2018

News media monitoring is an important scientific tool. By treating news reporters as data collectors and their reports as qualitative accounts of a fast changing public health landscape, researchers can glean many valuable insights. Yet, there have been surprisingly few innovations in public health media monitoring, with nearly all studies relying on labor-intensive content analyses limited to a small number of media reports. We propose to advance this subfield by using scalable machine learning. In potentially the largest contemporary public health media monitoring study to date, we systematically characterize global news reports surrounding electronic cigarettes or electronic nicotine delivery systems (ENDS) using natural language processing techniques. News reports including ENDS terms (e.g., "electronic cigarettes") from over 100,000 sources (all sources archived on Google News or Bing News, as well as all news articles shared on Twitter) were monitored for 1 January 2013 through 31 July 2018. The geographic and subject (e.g., prevalence, bans, quitting, warnings, marketing, prices, age, flavor and industry) foci of news articles, their popularity among readers who share news on social media, and the sentiment behind news articles were assessed algorithmically. Globally there were 86,872 ENDS news reports with coverage increasing from 8 (standard deviation [SD] = 8) stories per day in 2013 to 75 (SD = 56) stories per day during 2018. The focus of ENDS news spanned 148 nations, with the plurality focusing on the United States (34% of all news). Potentially overlooked hotspots of ENDS media activity included China, Egypt, Russia, Ukraine, and Paraguay. The most common subject was warnings about ENDS (18%), followed by bans on using ENDS (13%) and ENDS prices (9%). Flavor and age restrictions were the least covered news subjects (~1% each). Among different subject foci, reports on quitting cigarettes using ENDS had the highest probability of scoring in the top three deciles of popularity rankings. Moreover, ENDS news on quitting and prices had a more positive sentiment on average than news with other subject foci. Public health leaders can use these trends to stay abreast of how ENDS are portrayed in the media, and potentially how the public perceives ENDS. Because our analytical strategies are updated in near real time, we aim to make media monitoring part of standard practice to support evidence-based tobacco control in the future.

The Why We Retweet scale

Background

Twitter offers a platform for rapid diffusion of information and its users' attitudes and behaviors. Insights about information propagation via retweets (the message forwarding function) offer observable explanations of ways in which modern human interactions get organized in the form of online networks, and contextualized in the form of public health, policy decisions, disaster management, and civic participation. This study conceptualized and validated the Why We Retweet Scale to contextualize retweeting behavior.

Objective

Twitter users were identified using clustering algorithms that consider a users’ position in their network and invited for an online survey. Participants (N = 1433) responded to 19 questions about why they retweet. Exploratory factor Analysis (EFA) was conducted on a scale development sample (70% of original sample), which informed the Confirmatory Factor Analysis (CFA) on a scale testing sample (30% of the original sample). Varimax rotation was used to obtain a rotated factor solution, which resulted in interpretable factors. Demographic differences among scale factors were analyzed using one-way ANOVA or independent samples t-tests.

Results

The final model (χ²₂₁ = 28, RMSEA = .03 [90% CI, 0.00–0.06], CFA = .99, TLI = 0.99) represented a parsimonious solution with 4 factors, measured by 2–3 items each, creating a final scale consisting of 9 items. Factor labels and definitions were: (1) Show approval, “Show support to the tweeter”; (2) Argue, “To argue against a tweet that I disagree with”; (3) Gain attention, “Add followers or gain attention”; and (4) Entertain, “Create humor/amusement”. Demographic differences were also reported.

Conclusions

The Why We Retweet Scale offers a useful conceptualization and assessment of motivations for retweeting. In the future, communication strategists might consider the factors associated with information propagation when designing campaign messages to maximize message reach and engagement on Twitter.

Characterizing JUUL-related posts on Twitter

BACKGROUND

As vaping rapidly becomes more prevalent, social media data can be harnessed to capture individuals' discussions of e-cigarette products quickly. The JUUL vaporizer is the latest advancement in e-cigarette technology, which delivers nicotine to the user from a device that is the size and shape of a thumb drive. Despite JUUL's growing popularity, little research has been conducted on JUUL. Here we utilized Twitter data to determine the public's early experiences with JUUL describing topics of posts.

METHODS

Twitter posts containing the term "JUUL" were obtained for 1 April 2107 to 14 December 2017. Text classifiers were used to identify topics in posts (n = 81,689).

RESULTS

The most prevalent topic wasPerson Tagging (use of @username to tag someone in a post) at 20.48% followed by Pods (mentions of JUUL's refill cartridge) at 14.72% and Buying (mentions of purchases) at 10.49%. The topic School (posts indicative of using JUUL or seeing someone use JUUL while at elementary, middle, or high school) comprised 3.66% of posts. The topic of Quit Smoking was rare at 0.29%.

CONCLUSIONS

Data from social media may be used to extend the surveillance of newly introduced vaping products. Findings suggest Twitter users are bonding around, and inquiring about, JUUL on social media. JUUL's discreetness may facilitate its use in places where vaping is prohibited. Educators may be in need of training on how to identify JUUL in the classroom. Despite JUUL's branding as a smoking alternative, very few Twitter users mentioned smoking cessation with JUUL.

Talking about tobacco on Twitter is associated with tobacco product use

Tobacco-related content appears on social media in the form of advertising and messages by individuals. However, little is known about associations between posting social media messages and tobacco product use among adolescents and young adults. Self-reports of tobacco product use were obtained from the Children's Health Study of young adults in Southern California. Among the 1486 respondents in the most recent wave of the cohort (2016-2017), 284 provided tobacco product use data and their Twitter user names to access publicly available Twitter account data (mean age = 20.1 yrs. (SD = 0.6), 54% female, 49% Hispanic). We obtained the tweets that those respondents posted on Twitter, searched the tweets for 14 nicotine- and tobacco-related keywords, and coded these statements as positive or negative/neutral. Logistic regression analyses were conducted to determine whether respondents who posted positive tobacco-related tweets were more likely to report tobacco product use, relative to those who did not post any positive tobacco-related tweets. Respondents who posted any positive messages about tobacco had significantly higher odds of reporting past month use of cigarettes (OR = 3.15, 95% CI = 1.36, 7.30) and any tobacco product (OR = 2.41, 95% CI = 1.16, 5.01), relative to respondents who did not post about tobacco. This is the first study to establish an empirical link between adolescents' and young adults' tobacco-related Twitter activity and their tobacco product use. Health communications about the risks of tobacco use could target adolescents who post positive messages about tobacco products on Twitter.

Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study

BACKGROUND

Substance use is a major issue for adolescents and young adults, particularly college students. With the importance of peer influence and the ubiquitous use of social media among these age groups, it is important to assess what is discussed on various social media sites regarding substance use. One particular mobile app (Yik Yak) allowed users to post any message anonymously to nearby persons, often in areas with close proximity to major colleges and universities.

OBJECTIVE

This study describes the content, including attitude toward substances, of social media discussions that occurred near college campuses and involved substances.

METHODS

A total of 493 posts about drugs and alcohol on Yik Yak were reviewed and coded for their content, as well as the poster's attitude toward the substance(s) mentioned.

RESULTS

Alcohol (226/493, 45.8%), marijuana (206/493, 41.8%), and tobacco (67/493, 13%) were the most frequently mentioned substances. Posts about use (442/493) were generally positive toward the substance mentioned (262/442, 59.3%), unless the post was about abstinence from the substance. Additionally, posts that commented on the substance use of others tended to be less positive (18/92, 19.6% positive) compared to posts about one's own use (132/202, 65.3% positive).

CONCLUSIONS

This study provides a description of anonymous discussions on or near college campuses about drugs and alcohol, which serves as an example of data that can be examined from social media sites for further research and prevention campaigns.

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.