An Exploration of e-Cigarette–Related Search Items on YouTube: Network Analysis

Dashtian, Hassan; Murthy, Dhiraj; Kong, Grace

doi:10.2196/30679

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…We created 16 fictitious youth profiles to simulate youth searching for e-cigarettes on YouTube 17. For each fictitious youth profile, we used Orbot, a mobile app that uses an anonymised ‘Tor’ network, which is a set of servers around the world that relay traffic through each other to obfuscate Internet Protocol (IP) address and other information such as geographical location 18.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…We created 16 fictitious profiles on YouTube that sought to vary and reflect particular demographic attributes (ie, age, sex, and race) [ 32 ]. Profile photos were not added.…”

Section: Methodsmentioning

confidence: 99%

“…We collected video metadata such as title, description, transcript, view counts, likes/dislikes, comments, date published, channel name, and category. The methods are further explained in Dashtian et al [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

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Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification

Murthy¹,

Lee²,

Dashtian³

et al. 2023

JMIR Infodemiology

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Background The proliferation of e-cigarette content on YouTube is concerning because of its possible effect on youth use behaviors. YouTube has a personalized search and recommendation algorithm that derives attributes from a user’s profile, such as age and sex. However, little is known about whether e-cigarette content is shown differently based on user characteristics. Objective The aim of this study was to understand the influence of age and sex attributes of user profiles on e-cigarette–related YouTube search results. Methods We created 16 fictitious YouTube profiles with ages of 16 and 24 years, sex (female and male), and ethnicity/race to search for 18 e-cigarette–related search terms. We used unsupervised (k-means clustering and classification) and supervised (graph convolutional network) machine learning and network analysis to characterize the variation in the search results of each profile. We further examined whether user attributes may play a role in e-cigarette–related content exposure by using networks and degree centrality. Results We analyzed 4201 nonduplicate videos. Our k-means clustering suggested that the videos could be clustered into 3 categories. The graph convolutional network achieved high accuracy (0.72). Videos were classified based on content into 4 categories: product review (49.3%), health information (15.1%), instruction (26.9%), and other (8.5%). Underage users were exposed mostly to instructional videos (37.5%), with some indication that more female 16-year-old profiles were exposed to this content, while young adult age groups (24 years) were exposed mostly to product review videos (39.2%). Conclusions Our results indicate that demographic attributes factor into YouTube’s algorithmic systems in the context of e-cigarette–related queries on YouTube. Specifically, differences in the age and sex attributes of user profiles do result in variance in both the videos presented in YouTube search results as well as in the types of these videos. We find that underage profiles were exposed to e-cigarette content despite YouTube’s age-restriction policy that ostensibly prohibits certain e-cigarette content. Greater enforcement of policies to restrict youth access to e-cigarette content is needed.

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“…We included e-cigarette search words that have been shown to have the most connections to e-cigarette content on YouTube based on our previous study. (15) To be included in our dataset, the video had to: (1) be in English, (2) have content related to e-cigarettes and COVID-19, and (3) be less than 30 minutes in length (to ensure that we are capturing videos that people may watch). We also obtained video metadata (i.e., view counts, likes, dislikes, and upload date).…”

Section: Methodsmentioning

confidence: 99%

Content Analysis of YouTube Videos Related to E-cigarettes and COVID-19

Lee

Murthy

Kong

2023

Preprint

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INTRODUCTIONE-cigarettes are frequently promoted on social media and portrayed in ways that are attractive to youth. While COVID-19 pandemic significantly affected people’s lives, less known is how the pandemic influenced e-cigarette-related marketing and information on social media. This study identifies how e-cigarettes are portrayed during the COVID-19 pandemic on YouTube, one of the most popular social media platforms.METHODSWe searched for combinations of search terms related to e-cigarettes (i.e., “electronic cigarette”, “e-cigarette”, “e-cig”, “vape” and “vaping”) and COVID-19 (i.e., “corona”, “COVID”, “lockdown” and “pandemic”). To be included in the analysis, the video must be: uploaded after February 1, 2020, in English, related to e-cigarettes and COVID-19 and less than 30 minutes in length. We assessed video themes related to e-cigarettes and COVID-19, uploader characteristics, and featured e-cigarette products.RESULTSWe examined N=307 videos and found that N=220 (73.6%) were related to the health effects of e-cigarette use on COVID-19, followed by videos of how COVID-19 affects e-cigarette access/sales (N=40, 12.9%), and face mask-related videos (N=16, 5.1%) which included content regarding masks and e-cigarette use. Instructional videos on how to modify e-cigarettes to use with masks had the highest number of likes (Median=23; IQR=32) and comments (Median=10; IQR=7).CONCLUSIONSThis study identified various e-cigarette contents on YouTube during the COVID-19 pandemic. Our findings support the need for continuous surveillance on novel vaping-related content in reaction to policies and events such as the global pandemic on social media is needed.

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An Exploration of e-Cigarette–Related Search Items on YouTube: Network Analysis

Cited by 10 publications

References 43 publications

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

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

Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification

Content Analysis of YouTube Videos Related to E-cigarettes and COVID-19

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