Evidence map›Paper›PMID 39352739›Full record

ArticleJournal of medical Internet research2024

Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study.

Shuo-Yu Lin, Sahithi Kiran Tulabandu, J Randy Koch, Rashelle Hayes, Andrew Barnes, Hemant Purohit, Songqing Chen, Bo Han, Hong Xue

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Shuo-Yu LinDepartment of Health Administration and Policy, George Mason University, Fairfax, VA, United States.ORCID 0000-0003-4688-1424
Sahithi Kiran TulabanduWVU Health Affairs Institute, Morgantown, WV, United States.ORCID 0009-0000-6590-4228
J Randy KochDepartment of Psychology, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-8490-4100
Rashelle HayesDepartment of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-6734-3997
Andrew BarnesDepartment of Health Behavior and Policy, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0002-0357-3934
Hemant PurohitDepartment of Information Sciences and Technology, George Mason University, Fairfax, VA, United States.ORCID 0000-0002-4573-8450
Songqing ChenDepartment of Computer Science, George Mason University, Fairfax, VA, United States.ORCID 0000-0003-4650-7125
Bo HanDepartment of Computer Science, George Mason University, Fairfax, VA, United States.ORCID 0000-0001-7042-3322
Hong XueDepartment of Health Administration and Policy, George Mason University, Fairfax, VA, United States.ORCID 0000-0002-3641-6396

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgrounde-Cigarette (electronic cigarette) use has been a public health issue in the United States. On June 23, 2022, the US Food and Drug Administration (FDA) issued marketing denial orders (MDOs) to Juul Labs Inc for all their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media.

objectiveThis study aims to conduct a comprehensive analysis of tweets before and after the ban on Juul, aiming to shed light on public perceptions and sentiments surrounding this contentious topic and to better understand the life cycle of public health-related policy on social media.

methodsNatural language processing (NLP) techniques were used, including state-of-the-art BERTopic topic modeling and sentiment analysis. A total of 6023 tweets and 22,288 replies or retweets were collected from Twitter (rebranded as X in 2023) between June 2022 and October 2022. The encoded topics were used in time-trend analysis to depict the boom-and-bust cycle. Content analyses of retweets were also performed to better understand public perceptions and sentiments about this contentious topic.

resultsThe attention surrounding the FDA's ban on Juul lasted no longer than a week on Twitter. Not only the news (ie, tweets with a YouTube link that directs to the news site) related to the announcement itself, but the surrounding discussions (eg, potential consequences of this ban or block and concerns toward kids or youth health) diminished shortly after June 23, 2022, the date when the ban was officially announced. Although a short rebound was observed on July 4, 2022, which was contributed by the suspension on the following day, discussions dried out in 2 days. Out of the top 50 most retweeted tweets, we observed that, except for neutral (23/45, 51%) sentiment that broadcasted the announcement, posters responded more negatively (19/45, 42%) to the FDA's ban.

conclusionsWe observed a short life cycle for this news announcement, with a preponderance of negative sentiment toward the FDA's ban on Juul. Policy makers could use tactics such as issuing ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help maintain the conversation.

Indexed as

Electronic Nicotine Delivery SystemsNatural Language ProcessingSocial MediaUnited States Food and Drug AdministrationGovernment RegulationHumansPublic HealthPublic OpinionUnited Statesdata mininge-cigarette regulationmarketing denial ordersnatural language processingpublic health related policysentiment analysissocial media analysistopic modelingTwitter analysisvaping

Identifiers

PMID39352739
PMCPMC11480678

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.