ArticleTobacco prevention & cessation2017
A picture is worth a thousand words: Electronic cigarette content on Instagram and Pinterest.
Article in Tobacco prevention & cessation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- #TurntTrending: a systematic review of substance use portrayals on social media platforms.Addiction (Abingdon, England) · 2023Pooled it
- Perceptions and Sentiments About Electronic Cigarettes on Social Media Platforms: Systematic Review.JMIR public health and surveillance · 2020Pooled it
- Characterizing In Vivo Exposure to Alcohol Content in the Media: Media Platform, Engagement, and Associations With Attitudes.Alcohol, clinical & experimental research · 2026Article
- Instagram Memes of Oral Nicotine Pouches: Content Analysis Study.Online journal of public health informatics · 2026Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- Adolescents and Young Adults Use of Social Media and Following of e-Cigarette Influencers.Substance use & misuse · 2024Article
- Twitter Misinformation Discourses About Vaping: Systematic Content Analysis.Journal of medical Internet research · 2023Article
- Characterizing e-Cigarette-Related Videos on TikTok: Observational Study.JMIR formative research · 2023Article
- A Comprehensive Review of Vaping Use in Pediatric Patients and Recent Changes in Regulatory Laws.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2022Article
- Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study.JMIR public health and surveillance · 2021Observational
- Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook.International journal of environmental research and public health · 2021Article
- E-Cigarette Advertising in the UK: A Content Analysis of Traditional and Social Media Advertising to Observe Compliance with Current Regulations.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021Article
- Effects of Social Media on Adolescents' Willingness and Intention to Use E-Cigarettes: An Experimental Investigation.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021Article
- General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis.JMIR public health and surveillance · 2021Article
- Social media use by leading US e-cigarette, cigarette, smokeless tobacco, cigar and hookah brands.Tobacco control · 2020Article
- Electronic Cigarette-Related Contents on Instagram: Observational Study and Exploratory Analysis.JMIR public health and surveillance · 2020Observational
- E-Cigarette Promotion on Twitter in Australia: Content Analysis of Tweets.JMIR public health and surveillance · 2020Article
- #Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses.International journal of environmental research and public health · 2020Article
- E-Cigarette Advocates on Twitter: Content Analysis of Vaping-Related Tweets.JMIR public health and surveillance · 2020Article
- Young Adult Identification and Perception of Hashtag-Based Vaping Claims on Instagram.Health education & behavior : the official publication of the Society for Public Health Education · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
introductionThis study examined electronic cigarette (e-cig) content in visual materials posted on the social-media platforms Instagram and Pinterest. Both platforms allow users to upload pictures to the internet and share them globally. Users can search for pictures tagged with specific keywords and phrases.
methodsUsing content analysis, this study identified themes in image postings of e-cigs on social media. During five weeks of data collection, keywords were used to identify pictures related to e-cigs. These pictures were then coded into one or more categories.
resultsThe three most popular categories for Instagram posts were marketing, customization and juices/flavors. The three most popular categories for Pinterest posts were customization, marketing and memes.
conclusionsBecause of the persuasive power of visuals, it is important to examine communication on Instagram and Pinterest as well as the specific visual messages communicated. Stores and manufacturers use these and similar platforms to communicate with users and potential users; thus it seems that marketers are capitalizing on opportunities for persuasive appeal. The results highlight the popularity of e-cig content on these two social media platforms and reveal an emphasis on marketing and customization.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.