Evidence map›Paper›PMID 35611092›Full record

ArticleJMIR infodemiology

COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020.

J Lee Westmaas, Matthew Masters, Priti Bandi, Anuja Majmundar, Samuel Asare, W Ryan Diver

Abstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024
    Article
  3. 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

6 authors.

J Lee WestmaasPopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0002-6716-7695
Matthew MastersPopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0002-7245-8630
Priti BandiPopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0003-1169-4231
Anuja MajmundarPopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0002-7785-3357
Samuel AsarePopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0001-7888-0667
W Ryan DiverPopulation Science Department American Cancer Society Kennesaw, GA United States.ORCID https://orcid.org/0000-0002-5418-9000

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The risk of infection and severity of illness by SARS-CoV-2 infection is elevated for people who smoke cigarettes and may motivate quitting. Organic public conversations on Twitter about quitting smoking could provide insight into quitting motivations or behaviors associated with the pandemic. Objective: This study explored key topics of conversation about quitting cigarette smoking and examined their trajectory during 2018-2020. Methods: Topic model analysis with latent Dirichlet allocation (LDA) identified themes in US tweets with the term "quit smoking." The model was trained on posts from 2018 and was then applied to tweets posted in 2019 and 2020. Analysis of variance and follow-up pairwise tests were used to compare the daily frequency of tweets within and across years by quarter. Results: The mean numbers of daily tweets on quitting smoking in 2018, 2019, and 2020 were 133 (SD 36.2), 145 (SD 69.4), and 127 (SD 32.6), respectively. Six topics were extracted: (1) need to quit, (2) personal experiences, (3) electronic cigarettes (e-cigarettes), (4) advice/success, (5) quitting as a component of general health behavior change, and (6) clinics/services. Overall, the pandemic was not associated with changes in posts about quitting; instead, New Year's resolutions and the 2019 e-cigarette or vaping use-associated lung injury (EVALI) epidemic were more plausible explanations for observed changes within and across years. Fewer second-quarter posts in 2020 for the topic e-cigarettes may reflect lower pandemic-related quitting interest, whereas fourth-quarter increases in 2020 for other topics pointed to a late-year upswing. Conclusions: Twitter posts suggest that the pandemic did not generate greater interest in quitting smoking, but possibly a decrease in motivation when the rate of infections was increasing in the second quarter of 2020. Public health authorities may wish to craft messages for specific Twitter audiences (eg, using hashtags) to motivate quitting during pandemics.

Indexed as

COVID-19latent Dirichlet allocationmachine learningpandemicpublic healthquit smokingsmoking cessationsocial mediatopic model analysistweetTwitter

Identifiers

PMID35611092
PMCPMC9118581

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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.