Evidence map›Paper›PMID 42030728›Full record

ArticleSocial science & medicine (1982)2026

The role of social media influencer promotion in initiation and use of cigar, little cigar, cigarillo, and cannabis products among US youth and young adults: Using exogenous marketing exposure measures.

Ganna Kostygina, Yoonsang Kim, Mateusz Borowiecki, Chandler C Carter, Michael Liu, Alex Kresovich, Jennifer M Kreslake, Elizabeth C Hair, Sherry L Emery

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2026. 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.

Ganna KostyginaSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: kostygina-anna@norc.org.
Yoonsang KimSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: kim-yoonsang@norc.org.
Mateusz BorowieckiSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: borowiecki-mateusz@norc.org.
Chandler C CarterSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: carter-chandler@norc.org.
Michael LiuSchroeder Institute, Truth Initiative, Washington, DC, USA. Electronic address: mliu@truthinitiative.org.
Alex KresovichSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: kresovich-alex@norc.org.
Jennifer M KreslakeSchroeder Institute, Truth Initiative, Washington, DC, USA. Electronic address: jkreslake@truthinitiative.org.
Elizabeth C HairSchroeder Institute, Truth Initiative, Washington, DC, USA. Electronic address: ehair@truthinitiative.org.
Sherry L EmerySocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, Chicago, IL, USA. Electronic address: emery-sherry@norc.org.

Funding

Assessing the Effects of Cigar and Cigarillo Social Media Promotion on Tobacco and Marijuana UseR01CA248871 · NCI · NATIONAL OPINION RESEARCH CENTER · PI KOSTYGINA, GANNA · 2021 to 2025
$3.3M
NCI NIH HHS R01 CA248871
6 · The paper itself

Abstract

backgroundInfluencer marketing on social media is a powerful, yet under-examined, driver of youth and young adult substance use. Yet, reliably identifying influencer promotion and measuring its effects, particularly at community level, remains challenging. Influencers rarely disclose brand relationships, and conventional self-reported exposure metrics fail to capture the complex digital media environment and are vulnerable to recall and endogeneity biases. This study overcomes these limitations by employing exogenous localized measures of cigar, little cigar, and cigarillo (CLCC) influencer marketing to examine the effects of influencer messages on health behavior.

methodA corpus of over 48 million geolocated tweets was compiled using a combination of machine learning and human coding methods and integrated with retrospective data from a nationally representative survey of US individuals aged 15-21 (n = 9,555). Seven distinct metrics (including simple descriptive and network-based measures) were used to identify and quantify influential accounts that posted about CLCCs. County-level influencer tweet measures within four weeks before taking the survey were linked with the respondents' CLCC and cannabis use.

resultsMultivariate logistic regression analyses revealed that living in a county with any tweets from high-follower influencers was associated with 70% greater odds of current CLCC use (OR = 1.70) and 79% greater odds of current cannabis use (OR = 1.79). Among non-current users at baseline, potential exposure to high-follower influencers' tweets was associated with 263% greater odds of initiating current CLCC use by follow-up (OR = 3.63).

conclusionsThese findings indicate that county-level influencer marketing is associated with CLCC and cannabis use and initiation, and demonstrates spillover effects from tobacco to cannabis promotion. This work showcases a novel method for identifying social media influencers and assessing the effects of exposure to their posts on health behavior.

Indexed as

MarketingSocial MediaTobacco ProductsAdolescentFemaleHumansMaleMedia ExposureRetrospective StudiesUnited StatesYoung AdultComputational social scienceInfluencer marketingSocial mediaSubstance useTobacco control

Identifiers

PMID42030728
PMCPMC13224109

What OpenQuestion holds

Textmetadata
LicenceTDM
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.