Evidence map›Paper›PMID 31165716›Full record

ArticleJournal of medical Internet research2019

Tobacco Use Behaviors, Attitudes, and Demographic Characteristics of Tobacco Opinion Leaders and Their Followers: Twitter Analysis.

Kar-Hai Chu, Anuja Majmundar, Jon-Patrick Allem, Daniel W Soto, Tess Boley Cruz, Jennifer B Unger

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 14% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
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  6. Article
  7. Twitter Surveillance at the Intersection of the Triangulum.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022
    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

6 authors at 2 institutions in 1 country.

Kar-Hai Chu *University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0002-2486-8846
Anuja Majmundar *University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-7785-3357
Jon-Patrick Allem *University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-9135-8689
Daniel W Soto *University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-7602-7504
Tess Boley CruzUniversity of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-5894-1802
Jennifer B Unger *University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-9064-6603
University of Southern California · USUniversity of Pittsburgh · US

Funding

USC Tobacco Center of Regulatory ScienceU54CA180905 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Jennifer Beth Unger · 2018 to 2026
$39.0M
USC Tobacco Center of Regulatory Science (TCORS) for Vulnerable PopulationsP50CA180905 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI PENTZ, MARY ANN, SAMET, JONATHAN M · 2013 to 2017
$20.1M
Leveraging mobile applications to maximize tobacco cessationK07CA222338 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHU, KAR-HAI · 2018 to 2021
$585k
NCI NIH HHS K07 CA222338NCI NIH HHS P50 CA180905NCI NIH HHS U54 CA180905
6 · The paper itself

Abstract

backgroundTobacco-related content on social media is generated and propagated by opinion leaders on the Web who disseminate messages to others in their network, including followers, who then continue to spread the information. Opinion leaders can exert powerful influences on their followers' knowledge, attitudes, and behaviors; yet, little is known about the demographic characteristics and tobacco use behavior of tobacco opinion leaders on the Web and their followers, compared with general Twitter users.

objectiveIn this study, we hypothesized that opinion leaders use more tobacco products and have higher nicotine dependence than the other 2 groups (eg, followers and general Twitter users) and that followers-those who spread messages by opinion leaders-would more likely be in demographic groups that are vulnerable to tobacco marketing influence (eg, young adults and lower educational attainment).

methodsWe constructed the social networks of people who tweet about tobacco and categorized them using a combination of social network and Twitter metrics. To understand the characteristics of tobacco opinion leaders and their followers, we conducted a survey of tobacco opinion leaders, their followers, and general Twitter users. The sample included 347 opinion leaders, 567 followers, and 519 general users. The opinion leaders had a median of 1000 followers, whereas followers and general users had fewer than 600 followers.

resultsOpinion leaders were more likely than their followers to report past month use of tobacco products; followers, in turn, were more likely to report past month use of these products than general Twitter users. The followers appeared to be an especially vulnerable group; they tended to be younger (mean age 22.4 years) and have lower education compared with the opinion leaders and general users.

conclusionsFollowers of Twitter tobacco opinion leaders are a vulnerable group that might benefit from antitobacco education to counter the protobacco communications they see on social media.

Indexed as

AdultAttitudeDemographyHumansLeadershipSocial MediaSurveys and QuestionnairesTobacco UseYoung Adultonline social networkingpeer influencesocial mediasocial networkingtobacco

Identifiers

PMID31165716
PMCPMC6746100
OpenAlexW2941105224

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.