Characterizing vaping posts on Instagram by using unsupervised machine learning

Generative artificial intelligence and machine learning methods to screen social media content

Background

Social media research is confronted by the expansive and constantly evolving nature of social media data. Hashtags and keywords are frequently used to identify content related to a specific topic, but these search strategies often result in large numbers of irrelevant results. Therefore, methods are needed to quickly screen social media content based on a specific research question. The primary objective of this article is to present generative artificial intelligence (AI; e.g., ChatGPT) and machine learning methods to screen content from social media platforms. As a proof of concept, we apply these methods to identify TikTok content related to e-cigarette use during pregnancy.

Methods

We searched TikTok for pregnancy and vaping content using 70 hashtag pairs related to "pregnancy" and "vaping" (e.g., #pregnancytok and #ecigarette) to obtain 11,673 distinct posts. We extracted post videos, descriptions, and metadata using Zeeschuimer and PykTok library. To enhance textual analysis, we employed automatic speech recognition via the Whisper system to transcribe verbal content from each video. Next, we used the OpenCV library to extract frames from the videos, followed by object and text detection analysis using Oracle Cloud Vision. Finally, we merged all text data to create a consolidated dataset and entered this dataset into ChatGPT-4 to determine which posts are related to vaping and pregnancy. To refine the ChatGPT prompt used to screen for content, a human coder cross-checked ChatGPT-4's outputs for 10 out of every 100 metadata entries, with errors used to inform the final prompt. The final prompt was evaluated through human review, confirming for posts that contain "pregnancy" and "vape" content, comparing determinations to those made by ChatGPT.

Results

Our results indicated ChatGPT-4 classified 44.86% of the videos as exclusively related to pregnancy, 36.91% to vaping, and 8.91% as containing both topics. A human reviewer confirmed for vaping and pregnancy content in 45.38% of the TikTok posts identified by ChatGPT as containing relevant content. Human review of 10% of the posts screened out by ChatGPT identified a 99.06% agreement rate for excluded posts.

Conclusions

ChatGPT has mixed capacity to screen social media content that has been converted into text data using machine learning techniques such as object detection. ChatGPT's sensitivity was found to be lower than a human coder in the current case example but has demonstrated power for screening out irrelevant content and can be used as an initial pass at screening content. Future studies should explore ways to enhance ChatGPT's sensitivity.

Co-production of a youth advocacy video on the harms of e-cigarette advertising in Scotland

This study aimed to investigate young people's exposure to and perceptions of e-cigarette advertising to co-produce an advocacy video. This focus on e-cigarette marketing and its targeted appeal to young people comes at a crucial juncture, as policymakers in the UK and Scotland channel considerable efforts into shaping new regulations in response to these concerns, such as banning disposable e-cigarettes. The research to co-design a video was conducted with 33 young people aged between 12 and 16 living in the Central Belt of Scotland. The research comprised four stages: workshops, photo elicitation, focus groups and video development. Young people expressed concerns regarding the potential health effects of e-cigarettes, the ubiquity of e-cigarette advertising and products seemingly directed at young people, and the use of e-cigarettes among their peers. While none of our participants identified themselves as e-cigarette users, and all were below the age of 18, some mentioned seeing targeted advertisements for e-cigarettes online. These concerns were also reflected in participants' contributions to the video production process. Our findings highlight that young people feel overly exposed to e-cigarette advertising and they identified aspects of these adverts (including the use of vibrant colours and flavour variations) that they felt were designed to appeal specifically to young people. These findings suggest the need for stronger legislation to protect young people from the advertising and marketing of e-cigarettes. Further research might also usefully contribute to understanding counterarguments and marketing from public health advocates to limit the appeal of e-cigarettes to young people.

Generative AI in a new era of computer model-informed tobacco research: a short report

BACKGROUND

Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal of their promotional content, for example, depicting e-cigarettes alongside healthy lifestyle or entertainment imagery that could decrease youths' risk perceptions of e-cigarettes. Monitoring the prevalence of such content on social media using computer vision and generative AI (artificial intelligence) can provide valuable data for tobacco regulatory science (TRS).

METHODS

We selected 102 Instagram and TikTok videos posted by micro-influencers in 2021-2024 who promoted e-cigarettes alongside posts featuring four themes: cannabis, entertainment, fashion or healthy lifestyle. We used OpenAI's GPT-4o multimodal large-scale visual linguistic model to detect the presence of nicotine vaping, cannabis vaping, fashion, entertainment and healthy lifestyle. The model did not require any additional training and improved its performance as we modified the text prompt.

RESULTS

The model's accuracy was 87% for nicotine vaping, 96% for cannabis vaping, 99% for fashion, 96% for entertainment and 98% for healthy lifestyle.

CONCLUSIONS

Generative AI can achieve accurate object detection with zero-shot learning (no additional training of the pretrained model). This model can be applied to big data-scale sample sizes of images and videos to detect e-cigarette-related and other substance-related promotional content and contexts (eg, healthy lifestyle) used for the promotion of these products on social media, providing valuable data for TRS.

Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach

BACKGROUND

Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis such as image-clustering has been rarely used on TikTok. Image clustering can identify underlying patterns and structures across large sets of images, enabling more streamlined distillation and analysis of visual data on TikTok. This study used image-clustering approaches to examine e-cigarette-related images on TikTok.

METHODS

We searched for 13 hashtags related to e-cigarettes in November 2021 (e.g., vape, vapelife). We scraped up to 1000 posts per hashtag depending on the number of available posts, for 12,599 posts in total. After randomly selecting 13% of posts and excluding non-English (N = 278), non-e-cigarette-related (N = 88), and unavailable posts (i.e., posts that the uploader deleted) (N = 286), N = 838 e-cigarette TikTok images were included in our image clustering model. Using quantitative (e.g., silhouette scores) and qualitative evaluations, we categorized clusters into overarching themes based on the types of e-cigarette content depicted within each cluster.

RESULTS

We identified N = 20 clusters, forming four overarching themes: (1) vapor clouds (e.g., vape tricks, vaping and exhaling vapor clouds, being captured as clouds from the mouth or nose or around the face); (2) devices (e.g., content presenting e-cigarette devices or individuals demonstrating use or modification of devices); (3) text (e.g., e-cigarette-related text inserted within images such as jokes); (4) other (i.e., e-cigarette-related images clustered based on other image characteristics such as color tones).

CONCLUSIONS

This study using the state-of-the-art image-clustering method successfully identified various e-cigarette-related images on TikTok. This study suggests that novel methodologies can be helpful to tobacco regulatory agencies looking to conduct rapid surveillance of e-cigarette content on social media.

Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach

INTRODUCTION

The use of hashtags is a common way to promote e-cigarette content on social media. Analysis of hashtags may provide insight into e-cigarette promotion on social media. However, the examination of text data is complicated by the voluminous amount of social media data. This study used machine learning approaches (ie, Bidirectional Encoder Representations from Transformers [BERT] topic modeling) to identify e-cigarette content on TikTok.

AIMS AND METHODS

We used 13 unique hashtags related to e-cigarettes (eg, #vape) for data collection. The final analytic sample included 12 573 TikTok posts. To identify the best fitting number of topic clusters, we used both quantitative (ie, coherence test) and qualitative approaches (ie, researchers checked the relevance of text from each topic). We, then, grouped and characterized clustered text for each theme.

RESULTS

We evaluated that N = 18 was the ideal number of topic clusters. The 9 overarching themes were identified: Social media and TikTok-related features (N = 4; "duet," "viral"), Vape shops and brands (N = 3; "store"), Vape tricks (N = 3; "ripsaw"), Modified use of e-cigarettes (N = 1; "coil," "wire"), Vaping and girls (N = 1; "girl"), Vape flavors (N = 1; "flavors"), Vape and cigarettes (N = 1; "smoke"), Vape identities and communities (N = 1; "community"), and Non-English language (N = 3; Romanian and Spanish).

CONCLUSIONS

This study used a machine learning method, BERTopic modeling, to successfully identify relevant themes on TikTok. This method can inform future social media research examining other tobacco products, and tobacco regulatory policies such as monitoring of e-cigarette marketing on social media.

IMPLICATIONS

This study can inform future social media research examining other tobacco products, and tobacco regulatory policies such as monitoring of e-cigarette marketing on social media.

Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review

BACKGROUND

Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.

OBJECTIVE

This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.

METHODS

We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest.

RESULTS

This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella.

CONCLUSIONS

As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study

BACKGROUND

Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences.

OBJECTIVE

This study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtler strategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of social media posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok for youth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements on TikTok.

METHODS

We collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, #vapetok and #boxmod). Using the k-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on themes. Next, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machine learning-based model architectures: residual network (ResNet) with 50 layers (ResNet50), Visual Geometry Group model with 16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clustering output. To assess clustering accuracy, we examined 4.01% (441/10,990) of the samples from each video cluster. Finally, we randomly selected 50 videos (5% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation.

RESULTS

We successfully identified 5 major themes from the TikTok posts. Vape product marketing (1160/10,990, 8.28%) reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer (3775/14,002, 26.96%), general vape (2741/14,002, 19.58%), vape brands (2042/14,002, 14.58%), and vaping cessation (1272/14,002, 9.08%). The ResNet50 model successfully classified clusters based on image features, achieving an average F1-score of 0.97, the highest among the 3 models. Qualitative content analyses indicated that vaping was depicted as a normal, routine part of daily life, with TikTok influencers subtly incorporating vaping into popular culture (eg, gaming, skateboarding, and tattooing) and social practices (eg, shopping sprees, driving, and grocery shopping).

CONCLUSIONS

The results from both computational and qualitative analyses of text and visual data reveal that vaping is normalized on TikTok. Our identified themes underscore how everyday conversations, promotional content, and the influence of popular figures collectively contribute to depicting vaping as a normal and accepted aspect of daily life on TikTok. Our study provides valuable insights for regulatory policies and public health initiatives aimed at tackling the normalization of vaping on social media platforms.

Instagram for audience engagement: an evaluation of CERC framework in the GCC nations for digital public health during the Covid-19 pandemic

BACKGROUND

In this study, we investigate the utilization of Instagram by public health ministries across the Gulf Cooperation Council (GCC) nations to disseminate health-related information during the COVID-19 pandemic. With Instagram's visual-centric approach and high user engagement, the research aims to investigate its critical yet complex role in information dissemination amid a health crisis.

METHODS

To examine how Instagram communication strategies align with the CDC's Crisis and Emergency Risk Communication (CERC) framework, we employ the content analysis method. This approach helps to evaluate the effectiveness and challenges of employing Instagram for health communication within a region known for its significant social media usage.

RESULTS

Findings indicate that Instagram serves as a vital platform for the rapid dissemination of health information in the GCC, leveraging its visual capabilities and wide reach. The GCC ministries of health utilized Instagram to demonstrate a consistent and strategic approach to communicate essential COVID-19 related information. Kuwait and Bahrain were the most active of all the assessed ministries with respect to the number of engagement metrics (likes and comments). Most of the posts, as per the CERC framework, were informational and related to vaccine infection and death cases. The second most salient theme in line with the CERC framework was about promoting actions, followed by Instagram posts about activities, events, and campaigns.

CONCLUSIONS

The research underscores Instagram's potential as a powerful tool in enhancing public health resilience and responsiveness during health emergencies in the GCC. It suggests that leveraging social media, with careful consideration of its affordances, can contribute significantly to effective health communication strategies in times of crisis.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning

INTRODUCTION

Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence.

AIMS AND METHODS

We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts.

RESULTS

Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts.

CONCLUSION

Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use.

IMPLICATIONS

Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Using Computer Vision to Detect E-cigarette Content in TikTok Videos

INTRODUCTION

Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used social media platforms among youth, such as TikTok, contain primarily visual content, requiring the ability to detect e-cigarette-related content across large sets of videos and images. This study aims to use a computer vision technique to detect e-cigarette-related objects in TikTok videos.

AIMS AND METHODS

We searched 13 hashtags related to vaping on TikTok (eg, #vape) in November 2022 and obtained 826 still images extracted from a random selection of 254 posts. We annotated images for the presence of vaping devices, hands, and/or vapor clouds. We developed a YOLOv7-based computer vision model to detect these objects using 85% of extracted images (N = 705) for training and 15% (N = 121) for testing.

RESULTS

Our model's recall value was 0.77 for all three classes: vape devices, hands, and vapor. Our model correctly classified vape devices 92.9% of the time, with an average F1 score of 0.81.

CONCLUSIONS

The findings highlight the importance of having accurate and efficient methods to identify e-cigarette content on popular video-based social media platforms like TikTok. Our findings indicate that automated computer vision methods can successfully detect a range of e-cigarette-related content, including devices and vapor clouds, across images from TikTok posts. These approaches can be used to guide research and regulatory efforts.

IMPLICATIONS

Object detection, a computer vision machine learning model, can accurately and efficiently identify e-cigarette content on a primarily visual-based social media platform by identifying the presence of vaping devices and evidence of e-cigarette use (eg, hands and vapor clouds). The methods used in this study can inform computational surveillance systems for detecting e-cigarette content on video- and image-based social media platforms to inform and enforce regulations of e-cigarette content on social media.

An ecological examination of early adolescent e-cigarette use: A machine learning approach to understanding a health epidemic

E-cigarette use among adolescents is a national health epidemic spreading faster than researchers can amass evidence for risk and protective factors and long-term consequences associated with use. New technologies, such as machine learning, may assist prevention programs in identifying at risk youth and potential targets for intervention before adolescents enter developmental periods where e-cigarette use escalates. The present study utilized machine learning algorithms to explore a wide array of individual and socioecological variables in relation to patterns of lifetime e-cigarette use during early adolescence (i.e., exclusive, or with tobacco cigarettes). Extant data was used from 14,346 middle school students (Mage = 12.5, SD = 1.1; 6th and 8th grades) who participated in the Utah Prevention Needs Assessment. Students self-reported their substance use behaviors and related risk and protective factors. Machine learning algorithms examined 112 individual and socioecological factors as potential classifiers of lifetime e-cigarette use outcomes. The elastic net algorithm achieved outstanding classification for lifetime exclusive (AUC = .926) and dual use (AUC = .944) on a validation test set. Six high value classifiers were identified that varied in importance by outcome: Lifetime alcohol or marijuana use, perception of e-cigarette availability and risk, school suspension(s), and perceived risk of smoking marijuana regularly. Specific classifiers were important for lifetime exclusive (parent's attitudes regarding student vaping, best friend[s] tried alcohol or marijuana) and dual use (best friend[s] smoked cigarettes, lifetime inhalant use). Our findings provide specific targets for the adaptation of existing substance use prevention programs to address early adolescent e-cigarette use.

Youth's exposure to and engagement with e-cigarette marketing on social media: a UK focus group study

OBJECTIVE

Electronic-cigarettes (e-cigarette) are promoted creatively through social media and considering the potential influence of social media marketing on young people, we explored young people's exposure to and engagement with social media marketing of e-cigarettes.

DESIGN

Semistructured discussion groups.

SUBJECTS

Twenty focus groups with 82 young people aged 11-16 living in the Central belt of Scotland.

METHODS

Youths were asked about smoking and vaping behaviours, social media use, vaping advertisement exposure and were shown illustrative examples of social media content (eg, images and videos) about different messages, presentations and contextual features. Transcripts were imported into NVivo V.12, coded thematically and analysed.

RESULTS

Youths highlighted a variety of tactics e-cigarette companies use, including influencer or celebrity endorsement, attractive youth flavours, bright colours and emotional appeal to advertise and promote their products directly to young people. Social media influencers who advertise e-cigarettes were described as portraying e-cigarettes as 'cool' and 'fashionable' to entice viewers to try the products. Youths considered that there is a need for more restrictions on social media content to protect youths while also still allowing smokers to purchase them as a cessation device.

CONCLUSIONS

Our study highlights that the e-cigarette industry is using previously employed tactics similar to the tobacco industry to advertise and promote its products on social media. These findings suggest the growing need for governments to work together to develop and implement policies to restrict the advertising and marketing of e-cigarettes on social media.

Community and Opinion Leadership Effects on Vaping Discourse: A Network Analysis of Online Reddit Threads

Reddit is a popular hub for discussing vaping. A deeper understanding of the factors that influence this online discourse could inform public health messaging efforts targeting this platform. Using a network analysis framework, we sought to investigate the role of opinion leaders and online communities in facilitating vaping discussions on Reddit. We collected Reddit submissions about vaping posted in May 2021 and used these submissions to generate subreddit-level (N=261) and thread-level (N=8,377) data sets. We coded subreddits into four community categories: 1) Vaping, 2) Substance use, 3) Cessation, and 4) Non-specific. We used sociometric in-degree centrality statistics to identify subreddit opinion leaders. We computed non-parametric ANOVAs and negative binomial regressions to test associations between opinion leadership and subreddit community category variables on subreddit network composition (comprised of subreddit-level network nodes and edges) and the number of commenters on Reddit threads about vaping (thread-level). Subreddit network composition was largely dependent on opinion leaders in Non-specific communities, and less so in Vaping and Substance use communities. At the thread-level, the rate of commenters was higher among threads initiated by opinion leaders than non-opinion leaders (adjusted rate ratio [aRR]=4.84). Furthermore, threads posted in Vaping (aRR=1.64), Substance use (aRR=1.92), and Cessation (aRR=1.21) communities had higher commenter rates than those posted in Non-specific communities. Communities and opinion leaders play a key role in the composition and reach of vaping discussions on Reddit. These findings provide a foundation for public health campaigns and interventions targeting Reddit and perhaps other social media platforms.

User-generated content and influencer marketing involving e-cigarettes on social media: a scoping review and content analysis of YouTube and Instagram

BACKGROUND

Evidence suggests that experimentation with e-cigarettes among young people is increasing. Social media is widely used by young people with user-generated content and influencer marketing particularly influential in promoting products. This paper documents a snapshot of online user-generated content and influencer marketing related to e-cigarettes on YouTube and Instagram.

METHODS

Scoping review of relevant e-cigarette-related content on two social media platforms popular with youths, YouTube and Instagram, between June and August 2021. Content analysis was undertaken to examine text, audio, and video content, recording age restrictions, health warnings, page characteristics, and post characteristics. Narrative post content was coded using a coding frame that was developed inductively in response to emergent categories.

RESULTS

Vaping was portrayed positively on social media; of the posts analysed, 86.5% (n = 90 of 104) of Instagram posts and 66.0% (n = 64 of 97) of YouTube videos. Warnings about age restrictions and health (e.g., nicotine addiction/toxicity) did not feature in the majority of posts; 43.3% (n = 42) of YouTube videos (n = 42) contained an age warning compared to 20.2% of Instagram posts (n = 21). While 25.8% (n = 25) of YouTube videos and 21.2% of Instagram (n = 22) posts contained a health warning.

CONCLUSION

Of concern is the fact that the vast majority of YouTube and Instagram content about e-cigarettes promoted their use, and typically the content does not contain age and/or health warnings. These findings may highlight a priority for governmental policy to restrict the ability of marketers to reach youths with social media content promoting e-cigarettes.

Machine learning applications in tobacco research: a scoping review

OBJECTIVE

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

DATA SOURCES

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

STUDY SELECTION

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

DATA EXTRACTION

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

DATA SYNTHESIS

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

CONCLUSIONS

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

#Nomask on Instagram: Exploring Visual Representations of the Antisocial Norm on Social Media

Social media (SM) functions such as hashtags and photo uploading can enrich and expedite user interactions, but can also facilitate the online spread of antisocial norms. Mask aversion is one such antisocial norm shared on SM in the current COVID-19 pandemic circumstances. This study utilized the social representation theory (SRT) to explore how mask aversion is visually represented in the Instagram photos tagged with #NoMask. It examined the overall content of the photos, the characteristics of the faces portrayed in the photos, and the presented words in the photos. Additionally, the study grouped the photos through k-means clustering and compared the resulting clusters in terms of content, characteristics of the faces, presented words, pixel-level characteristics, and the public’s responses to the photos. The results indicate that people, especially human faces, were visually represented the most in the Instagram photos tagged with #NoMask. Two clusters were generated by k-means clustering—Text-centered and people-centered. The visual representations of the two clusters differed in terms of content characteristics and pixel-level attributes. The texts presented in the photos manifested a unique way of delivering key messages. The photos of the people-centered cluster received more positive comments than the text-centered one; however, the two clusters were not significantly different in eliciting engagement. This study can contribute to expanding the scope of SRT to visual representations and hashtag movements.

Exposure to tobacco and e-cigarette advertisements by sexual identity status among high school students

INTRODUCTION

The literature on tobacco advertising among sexual minorities is relatively scarce. This study examined the association between exposure to tobacco products and e-cigarettes advertisements and sexual identity.

METHODS

Data were from the 2020 National Youth Tobacco Survey (n = 7223). The prevalence of exposure to tobacco marketing through various channels was estimated among high school students and by sexual identity subgroups. Multivariable logistic regressions were used to examine the association between sexual identity status and exposure to tobacco and e-cigarette advertisements.

RESULTS

The proportion of sexual minority adolescents who reported exposure to tobacco and e-cigarette advertisements was higher than heterosexuals. In multivariable analysis, gay or lesbian youth (aOR 1.45, 95% CI, 1.04-2.02) had higher odds of any exposure to tobacco and e-cigarette advertisement than heterosexuals. Regarding the channel of advertisement exposure, sexual minorities were more likely to be exposed via newspapers/magazines for cigarettes or other tobacco products. Likewise, gay or lesbian youth and those not sure about their sexual identity had higher odds of exposure via newspapers/magazines for e-cigarettes. Analysis stratified by sex showed significant differences, with gay or lesbian males more likely to be exposed to any tobacco and e-cigarette advertisements via the internet, newspapers/magazines, and TV/streaming services. Males not sure about their sexual identity are more likely to be exposed to tobacco and e-cigarette advertisements via newspapers/magazines.

CONCLUSIONS

Overall, sexual minority adolescents had high exposure to tobacco products and e-cigarette advertisements, especially males. Prevention and intervention efforts targeting this population could help reduce the well-established tobacco use disparities.

Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram

BACKGROUND

Increased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms.

OBJECTIVES

We used unsupervised machine learning to identify and characterise sale offers of electronic nicotine delivery systems (ENDS) and associated products on Instagram. We examined types of sellers, geographic ENDS location and use of age verification.

METHODS

Our methodology was composed of three phases: data collection, topic modelling and content analysis. We used data mining approaches to query hashtags related to ENDS product use among young adults to collect Instagram posts. For topic modelling, we applied an unsupervised machine learning approach to thematically categorise and identify topic clusters associated with selling activity. Content analysis was then used to characterise offers for sale of ENDS products.

RESULTS

From 70 725 posts, we identified 3331 engaged in sale of ENDS products. Posts originated from 20 different countries and were roughly split between individual (46.3%) and retail sellers (43.4%), with linked online sellers (8.8%) representing a smaller volume. ENDS products most frequently offered for sale were flavoured e-liquids (53.0%) and vaping devices (20.5%). Online sellers offering flavoured e-liquids were less likely to use age verification at point of purchase (29% vs 64%) compared with other products.

CONCLUSIONS

Instagram is a global venue for unregulated ENDS sales, including flavoured products, and access to websites lacking age verification. Such posts may violate Instagram's policies and US federal and state law, necessitating more robust review and enforcement to prevent ENDS uptake and access.

Characterizing HIV discussions and engagement on Twitter

The novel settings provided by social media facilitate users to seek and share information on a wide array of subjects, including healthcare and wellness. Analyzing health-related opinions and discussions on these platforms complement traditional public health surveillance systems to support timely and effective interventions. This study aims to characterize the HIV-related conversations on Twitter by identifying the prevalent topics and the key events and actors involved in these discussions. Through Twitter API, we collected tweets containing the hashtag #HIV for a one-year period. After pre-processing the collected data, we conducted engagement analysis, temporal analysis, and topic modeling algorithm on the analytical sample (n = 122,807). Tweets by HIV/AIDS/LGBTQ activists and physicians received the highest level of engagement. An upsurge in tweet volume and engagement was observed during global and local events such as World Aids Day and HIV/AIDS awareness and testing days for trans-genders, blacks, women, and the aged population. Eight topics were identified that include “stigma”, “prevention”, “epidemic in the developing countries”, “World Aids Day”, “treatment”, “events”, “PrEP”, and “testing”. Social media discussions offer a nuanced understanding of public opinions, beliefs, and sentiments about numerous health-related issues. The current study reports various dimensions of HIV-related posts on Twitter. Based on the findings, public health agencies and pertinent entities need to proactively use Twitter and other social media by engaging the public through involving influencers. The undertaken methodological choices may be applied to further assess HIV discourse on other popular social media platforms.

Videos With the Hashtag #vaping on TikTok and Implications for Informed Decision-making by Adolescents: Descriptive Study

BACKGROUND

Despite the public health importance of vaping and the widespread use of TikTok by adolescents and young adults, research is lacking on the nature and scope of vaping content on this networking service.

OBJECTIVE

The purpose of this study is to describe the content of TikTok videos related to vaping.

METHODS

By searching the hashtag #vaping in the discover feature, ~478.4 million views were seen during the time of data collection. The first 100 relevant videos under that hashtag were used in this study. Relevance was determined by simply noting if the video was related in any way to vaping. Coding consisted of several categories directly related to vaping and additional categories, including the number of likes, comments, and views, and if the video involved music, humor, or dance.

RESULTS

The 100 videos included in the sample garnered 156,331,347 views; 20,335,800 likes; and 296,460 comments. The majority of the videos (n=59) used music and over one-third (n=37) used humor. The only content category observed in the majority of the videos sampled was the promotion of vaping, which was included in 57 videos that garnered over 74 million views (47.5% of cumulative views). A total of 42% (n=42) of the 100 videos sampled featured someone vaping or in the presence of vape pens, and these videos garnered over 22% (>35 million) of the total views.

CONCLUSIONS

It is necessary for public health agencies to improve understanding of the nature and content of videos that attract viewers' attention and harness the strength of this communication channel to promote informed decision-making about vaping.

Public health agencies outreach through Instagram during the COVID-19 pandemic: Crisis and Emergency Risk Communication perspective

BACKGROUND

Governmental and non-governmental institutions increasingly use social media as a strategic tool for public outreach. Global spread, promptness, and dialogic potentials make these platforms ideal for public health monitoring and emergency communication in crises such as COVID-19.

OBJECTIVE

Drawing on the Crisis and Emergency Risk Communication framework, we sought to examine how leading health organizations use Instagram for communicating and engaging during the COVID-19 pandemic.

METHODS

We manually retrieved Instagram posts together with relevant metadata of four health organizations (WHO, CDC, IFRC, and NHS) shared between January 1, 2020, and April 30, 2020. Two coders manually coded the analytical sample of 269 posts related to COVID-19 on dimensions including content theme, gender depiction, person portrayal, and image type. We further analyzed engagement indices associated with the coded dimensions.

RESULTS

The CDC and WHO were the most active of all the assessed organizations with respect to the number of posts, reach, and engagement indices. Most of the posts were about personal preventive measures and mitigation, general advisory and vigilance, and showing gratitude and resilience. An overwhelming level of engagement was observed for posts representing celebrity, clarification, and infographics.

CONCLUSIONS

Instagram can be an effective tool for health organizations to convey their messages during crisis communication, notably through celebrity involvement, clarification posts, and the use of infographics. There is much opportunity to strengthen the role of health organizations in countering misinformation on social media by providing accurate information, directing users to credible sources, and serving as a fact-check for false information.

Modelling Public Sentiments about Juul Flavors on Twitter through Machine Learning

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

IMPLICATIONS

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

Association Between Social Media Use and Vaping Among Florida Adolescents, 2019

INTRODUCTION

With the growing popularity of vaping, evidence has emerged about the association between social media use and vaping among adolescents, possibly because of the proliferation of e-cigarette advertisements and other related content on social media. Our study examined the association between social media use and vaping among adolescents.

METHODS

Using data from the 2019 Florida Youth Tobacco Survey (N = 10,776), we conducted logistic regression models on adolescent vaping status (experimental and current vaping) by nondaily and daily use of social media platforms - Facebook, Instagram, Twitter and Snapchat, controlling for other confounders.

RESULTS

Use of all 4 selected social media platforms was significantly associated with vaping status (P <.001 for all). Once jointly analyzed, daily use of Instagram was significantly associated with increased relative risks of experimental (adjusted relative risk ratio [aRRR] = 1.76; 95% CI, 1.38-2.25) and current vaping (aRRR = 1.51; 95% CI, 1.16-1.95); nondaily use of Snapchat was significantly associated with increased relative risk of experimental (aRRR = 1.57; 95% CI, 1.17-2.10) and current vaping (aRRR = 1.87; 95% CI, 1.31-2.66); daily use of Snapchat was associated with increased relative risk of experimental (aRRR = 2.38; 95% CI, 1.85-3.08) and current vaping (aRRR = 5.09; 95% CI, 3.78-6.86); nondaily use of Facebook was associated with increased relative risk of current vaping (aRRR = 1.20; 95% CI, 1.00-1.43), and nondaily use of Twitter was associated with increased relative risk of current vaping (aRRR = 1.29; 95% CI, 1.07-1.56).

CONCLUSION

Multilevel efforts are warranted to monitor social media use and vaping status among adolescents, including media use monitoring plans, developing counter-marketing campaigns, and strict regulatory action on social media.

Vaping and Instagram: A Content Analysis of e-Cigarette Posts Using the Content Appealing to Youth (CAY) Index

The promotion of flavors, perceptions of "coolness," and general curiosity are characteristics of electronic nicotine delivery systems (ENDS) that have appealed to young adults. However, little is known about the characteristics of popular social media posts related to ENDS on the social media network, Instagram. Methods: Content analysis was performed using the Content Appealing to Youth (CAY) index. Over 700 posts were collected from August 2019 - December 2019 by searching the Instagram hashtags, #vape and #vapelife. Frequencies and percentages were calculated for each of the six major categories and 35 sub-categories. Results: Nearly all of the images were color photographs and 84% featured an ENDS device (mod) as the focal point. The style of the device was often matte (75%) in only one or two main colors (55%). Warnings about age restrictions and nicotine were included in 28% of images, but commonly used promotional tactics, such as humor, presence of vapor puffs, and flavors were rarely utilized. Conclusions: Instagram posts featuring ENDS are visually appealing and like cigarette packaging, may have the capacity to influence perceptions about the product. Since it is culturally normative for appealing images to be shared on Instagram, greater attention should be placed on media literacy skills to educate young adults about ENDS viewed on social media.

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.