Evidence map›Paper›PMID 38990633›Full record

ArticleJMIR formative research2024

Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study.

Pinxin Liu, Xubin Lou, Zidian Xie, Ce Shang, Dongmei Li

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Pinxin Liu *Department of Computer Science, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0009-0009-6538-7174
Xubin Lou *Goergen Institute for Data Science, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0009-0009-6227-3913
Zidian Xie *Department of Clinical and Translational Research, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-5149-7710
Ce ShangCenter for Tobacco Research, The Ohio State University Wexner Medical Center, Columbus, OH, United States.ORCID https://orcid.org/0000-0002-8838-4250
Dongmei LiDepartment of Clinical and Translational Research, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0001-9140-2483

Funding

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

Abstract

backgroundOn June 23, 2022, the US Food and Drug Administration announced a JUUL ban policy, to ban all vaping and electronic cigarette products sold by Juul Labs.

objectiveThis study aims to understand public perceptions and discussions of this policy using Twitter (subsequently rebranded as X) data.

methodsUsing the Twitter streaming application programming interface, 17,007 tweets potentially related to the JUUL ban policy were collected between June 22, 2022, and July 25, 2022. Based on 2600 hand-coded tweets, a deep learning model (RoBERTa) was trained to classify all tweets into propolicy, antipolicy, neutral, and irrelevant categories. A deep learning model (M3 model) was used to estimate basic demographics (such as age and gender) of Twitter users. Furthermore, major topics were identified using latent Dirichlet allocation modeling. A logistic regression model was used to examine the association of different Twitter users with their attitudes toward the policy.

resultsAmong 10,480 tweets related to the JUUL ban policy, there were similar proportions of propolicy and antipolicy tweets (n=2777, 26.5% vs n=2666, 25.44%). Major propolicy topics included "JUUL causes youth addition," "market surge of JUUL," and "health effects of JUUL." In contrast, major antipolicy topics included "cigarette should be banned instead of JUUL," "against the irrational policy," and "emotional catharsis." Twitter users older than 29 years were more likely to be propolicy (have a positive attitude toward the JUUL ban policy) than those younger than 29 years.

conclusionsOur study showed that the public showed different responses to the JUUL ban policy, which varies depending on the demographic characteristics of Twitter users. Our findings could provide valuable information to the Food and Drug Administration for future electronic cigarette and other tobacco product regulations.

Indexed as

deep learninge-cigarettesFDAFood and Drug AdministrationJUULregulationsmokingsocial mediaTwittervapevaping

Identifiers

PMID38990633
PMCPMC11273066

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.