Evidence map›Paper›PMID 34372926›Full record

ArticleBMC research notes2021

Characterizing alternative and emerging tobacco product transition of use behavior on Twitter.

Cortni Bardier, Joshua S Yang, Jiawei Li, Tim K Mackey

Abstract read
In one paragraph

Article in BMC research notes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Cortni BardierGlobal Health Policy and Data Institute, San Diego, CA, USA.
Joshua S YangDepartment of Public Health, California State University, Fullerton, Fullerton, CA, USA.
Jiawei LiGlobal Health Policy and Data Institute, San Diego, CA, USA.
Tim K MackeyGlobal Health Policy and Data Institute, San Diego, CA, USA. tmackey@ucsd.edu.ORCID http://orcid.org/0000-0002-2191-7833

Funding

Tobacco-Related Disease Research Program Awards #T29IP0465 and #T29IP0384
6 · The paper itself

Abstract

objectiveThe objective of this study was to develop an inductive coding approach specific to characterizing user-generated social media conversations about transition of use of different tobacco and alternative and emerging tobacco products (ATPs).

resultsA total of 40,206 tweets were collected from the Twitter public API stream that were geocoded from 2018 to 2019. Using data mining approaches, these tweets were then filtered for keywords associated with tobacco and ATP use behavior. This resulted in a subset of 5718 tweets, with 657 manually annotated and identified as associated with user-generated conversations about tobacco and ATP use behavior. The 657 tweets were coded into 9 parent codes: inquiry, interaction, observation, opinion, promote, reply, share knowledge, use characteristics, and transition of use behavior. The highest number of observations occurred under transition of use (43.38%, n = 285), followed by current use (39.27%, n = 258), opinions about use (0.07%, n = 46), and product promotion (0.06%, n = 37). Other codes had less than ten tweets that discussed these themes. Results provide early insights into how social media users discuss topics related to transition of use and their experiences with different and emerging tobacco product use behavior.

Indexed as

Social MediaTobacco ProductsData MiningHumansTobacco UseElectronic cigarettesQualitative researchSocial mediaTobacco behaviorTwitter

Identifiers

PMID34372926
PMCPMC8351350

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LicenceCC BY
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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.