Evidence map›Paper›PMID 33151157›Full record

Observational studyJMIR public health and surveillance2020

Electronic Cigarette-Related Contents on Instagram: Observational Study and Exploratory Analysis.

Yankun Gao, Zidian Xie, Li Sun, Chenliang Xu, Dongmei Li

Abstract readObservational Study
In one paragraph

Observational study in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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  6. Article
  7. Article
  8. Article
  9. Vaping: Public Health, Social Media, and Toxicity.Online journal of public health informatics · 2024
    Article
  10. Article
  11. Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yankun GaoUniversity of Rochester Medical Center, Rochester, NY, United States.ORCID 0000-0002-0585-967X
Zidian XieUniversity of Rochester Medical Center, Rochester, NY, United States.ORCID 0000-0002-5149-7710
Li SunUniversity of Rochester, Rochester, NY, United States.ORCID 0000-0001-7184-0232
Chenliang XuUniversity of Rochester, Rochester, NY, United States.ORCID 0000-0002-2183-822X
Dongmei LiUniversity of Rochester Medical Center, Rochester, NY, United States.ORCID 0000-0001-9140-2483

Funding

WNY Center for Research on Flavored Tobacco Products (CRoFT)U54CA228110 · NCI · ROSWELL PARK CANCER INSTITUTE CORP · PI LI, DONGMEI · 2018 to 2022
$19.8M
NCI NIH HHS U54 CA228110
6 · The paper itself

Abstract

backgroundInstagram is a popular social networking platform for users to upload pictures sharing their experiences. Instagram has been widely used by vaping companies and stores to promote electronic cigarettes (e-cigarettes), as well as by public health entities to communicate the risks of e-cigarette use (vaping) to the public.

objectiveWe aimed to characterize current vaping-related content on Instagram through descriptive analyses.

methodsFrom Instagram, 42,951 posts were collected using vaping-related hashtags in November 2019. The posts were grouped as (1) pro-vaping, (2) vaping warning, (3) neutral to vaping, and (4) not related to vaping based on the attitudes to vaping expressed within the posts. From these Instagram posts and the corresponding 18,786 unique Instagram user accounts, 200 pro-vaping and 200 vaping-warning posts as well as 200 pro-vaping and 200 vaping-warning user accounts were randomly selected for hand coding. Furthermore, follower counts and media counts of the Instagram user accounts as well as the "like" counts and hashtags of the posts were compared between pro-vaping and vaping-warning groups.

resultsThere were more posts in the pro-vaping group (41,412 posts) than there were in the vaping-warning group (1539 posts). The majority of pro-vaping images were product display images (163/200, 81.5%), and the most popular image type in vaping-warning posts was educational (95/200, 47.5%). The highest proportion of pro-vaping user account type was vaping store (110/189, 58.1%), and the store account type had the highest mean number of posts (10.33 posts/account). The top 3 vaping-warning user account types were personal (79/155, 51%), vaping-warning community (37/155, 23.9%), and community (35/155, 22.6%), of which the vaping-warning community had the highest mean number of posts (3.68 posts/account). Pro-vaping user accounts had more followers (median 850) and media (median 232) than vaping-warning user accounts had (follower count: median 191; media count: 92). Pro-vaping posts had more "likes" (median 22) and hashtags (mean 20.39) than vaping-warning posts had ("like" count: median 12; hashtags: mean 7.16).

conclusionsInstagram is dominated by pro-vaping content, and pro-vaping posts and user accounts seem to have more user engagement than vaping-warning accounts have. These results highlight the importance of regulating e-cigarette posts on social media and the urgency of identifying effective communication content and message delivery methods with the public about the health effects of e-cigarettes to ameliorate the epidemic of vaping in youth.

Indexed as

Electronic Nicotine Delivery SystemsHealth PromotionHumansSocial Mediae-cigelectronic cigarettesexploratoryinfodemiologyInstagrampublic healthrisksmokingsocial mediauser engagementvapevaping

Identifiers

PMID33151157
PMCPMC7677028

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.