Machine learning applications in tobacco research: a scoping review

Fu, Rui; Kundu, Anasua; Mitsakakis, Nicholas; Elton‐Marshall, Tara; Wang, Wei; Hill, Sean; Bondy, Susan; Hamilton, Hayley; Selby, Peter; Schwartz, Robert; Chaiton, Michael

doi:10.1136/tobaccocontrol-2020-056438

Cited by 35 publications

(24 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent scoping review found that 32 of 74 studies that used ML to understand tobacco content examined social media 13. Most of the studies in this review examined Twitter and only one study examined YouTube, which identified that 97% of YouTube videos had protobacco content 14.…”

Section: Introductionmentioning

confidence: 95%

Understanding e-cigarette content and promotion on YouTube through machine learning

et al. 2022

View full text Add to dashboard Cite

IntroductionYouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products.MethodsWe identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models.Results3830 English videos were included in the supervised machine learning. The most common video theme was ‘product review’ (48.9%), followed by ‘instruction’ (eg, ‘how to’ use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; ‘vape enthusiasts’ most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). ‘Vape trick’ was the least common theme but had the highest engagement (eg, >2 million views). ‘Cannabis’ (53.9%) and ‘instruction’ (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87.DiscussionOur findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.

show abstract

Section: Introductionmentioning

confidence: 95%

Understanding e-cigarette content and promotion on YouTube through machine learning

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Although there are studies that have applied machine learning methods such as classification trees 13 and random forest 14 in tobacco research, a recent scoping review suggested that these applications are rarely linked to public health impacts. 15 Thus, the aim of our study was to investigate further ever-vaping and daily vaping (as a proxy for vaping dependence) among the youth population, using machine learning methods with interpretable findings. In particular, our objectives were to develop machine learning algorithms that predict both ever-vaping and daily vaping among Ontario youth, and to perform post hoc analysis including ranking the importance of individual risk factors on both outcomes and illustrating statistical intersections to identify particularly susceptible youth subgroups.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to predict e-cigarette use and dependence among Ontario youth

Shi

Hamilton

et al. 2022

Health Promot Chronic Dis Prev Can

Self Cite

View full text Add to dashboard Cite

Introduction We developed separate random forest algorithms to predict e-cigarette (vaping) ever use and daily use among Ontario youth, and subsequently examined predictor importance and statistical interaction. Methods This cross-sectional study used a representative sample of Ontario elementary and high school students in 2019 (N = 6471). Vaping frequency over the last 12 months was used to define ever-vaping and daily vaping. We considered a large set of individual characteristics as potential correlates for ever-vaping (176 variables) and daily vaping (179 variables). Using cross-validation, we developed random forest algorithms and evaluated model performance based on the C-index, a measure to assess the discriminatory ability of a model, for both outcomes. Further, the top 10 correlates were identified by relative importance score calculation and their interaction with sociodemographic characteristics. Results There were 2064 (31.9%) ever-vapers, and 490 (7.6%) of the respondents were daily users. The random forest algorithms for both outcomes achieved high performance, with C-index over 0.90. The top 10 correlates of daily vaping included use of caffeine, cannabis and tobacco, source and type of e-cigarette and absence in last 20 school days. Those of ever-vaping included school size, use of alcohol, cannabis and tobacco; 9 of the top 10 ever-vaping correlates demonstrated interactions with ethnicity. Conclusion Machine learning is a promising methodology for identifying the risks of ever-vaping and daily vaping. Furthermore, it enables the identification of important correlates and the assessment of complex intersections, which may inform future longitudinal studies to customize public health policies for targeted population subgroups.

show abstract

“…We aim to address these limitations in the current analysis using machine learning, a group of computationally-intensive and data-driven analytical methods that has gained increasing popularity in health research [18][19][20], including in the research of smoking cessation [21][22][23] and behaviours of vaping [24,25]. Compared to conventional regression, machine learning has strengths in mitigating the risk of model overfitting and producing highly accurate and robust prediction [18].…”

Section: Introductionmentioning

confidence: 99%

Predictors of perceived success in quitting smoking by vaping: A machine learning approach

Schwartz

Mitsakakis

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.

show abstract

Machine learning applications in tobacco research: a scoping review

Cited by 35 publications

References 105 publications

Understanding e-cigarette content and promotion on YouTube through machine learning

Understanding e-cigarette content and promotion on YouTube through machine learning

A machine learning approach to predict e-cigarette use and dependence among Ontario youth

Predictors of perceived success in quitting smoking by vaping: A machine learning approach

Contact Info

Product

Resources

About