Evidence map›Paper›PMID 41886744›Full record

Observational studyJournal of medical Internet research2026

Opposition to Youth e-Cigarette Prevention Campaigns on Twitter and TikTok: Cross-Platform Observational Mixed Methods Analysis.

Chandler C Carter, Simon Page, Mateusz Borowiecki, Ganna Kostygina, Sherry L Emery, Miao Feng

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chandler C CarterSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0000-0001-8347-5201
Simon PageSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0009-0009-5433-3509
Mateusz BorowieckiSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0000-0002-2079-0658
Ganna KostyginaSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0000-0002-8416-6168
Sherry L EmerySocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0000-0001-9278-9990
Miao FengSocial Data Collaboratory, Public Health Department, NORC at the University of Chicago, 55 E Monroe St 30th Floor, Chicago, IL, 60603, United States, 1 3127594000.ORCID http://orcid.org/0000-0001-7206-8440

Funding

Using Innovative Machine Learning to Detect Organized Support and Opposition to E-cigarette Use Prevention Campaign Messaging on Twitter and TikTokR01CA283038 · NCI · NATIONAL OPINION RESEARCH CENTER · PI Miao Feng · 2023 to 2026
$2.8M
NCI NIH HHS R01 CA283038
6 · The paper itself

Abstract

Background: Youth e-cigarette use rose sharply between 2013 and 2024 in the United States, prompting widespread prevention campaigns at national, state, and local levels. However, many campaigns encountered online opposition, sometimes leading to message distortion or campaign withdrawal. While previous studies have examined individual campaigns, little is known about how oppositional dynamics differ across social media platforms with distinct architectures. Objective: This study aimed to conduct a retrospective, cross-platform surveillance study of oppositional responses to US youth e-cigarette prevention campaigns, comparing tactics, themes, and engagement on Twitter (now X) and TikTok to inform platform-specific public health strategies. Methods: We collected Twitter (2014-2020) and TikTok (2020-2023) posts related to major US e-cigarette prevention campaigns using 15 campaign-specific hashtags and 4 verified prevention campaign handles. We included public, English-language posts from geographic regions allowed by the platforms. Machine learning classification and human coding were used to detect oppositional content, characterize narrative frames, and classify user types. Engagement was assessed using post-level metrics, including likes, shares, comments, and retweets. We analyzed message prevalence, engagement patterns, and oppositional themes. Results: On Twitter, opposition comprised 26.8% (83,074/310,207) of campaign-related posts overall but dominated certain campaigns (eg, Still Blowing Smoke: 6052/6113, 99%). A small cluster of advocacy and commercial accounts generated 57.8% of opposition retweets. Dominant narratives included questioning the credibility of health authorities, claims that prevention advertisements backfired, vaping rights, and product promotion. In contrast, TikTok opposition constituted only 3.5% (108/3127, 95% CI 3.1%-3.9%) of posts and was characterized by humor (71/108, 65.7%), mockery (48/108, 44.4%), and ironic portrayals of vaping (30/108, 27.8%). Individual creators comprised 76.1% (153/201) of accounts sharing prevention posts, and opposition videos used the visibility-boosting hashtag #fyp significantly more than prevention posts (51.9% vs 32.2%; P<.001). Despite inconsistent hashtag use, prevention posts achieved higher average engagement than oppositional content. Conclusions: This novel cross-platform, multicampaign analysis of opposition responses to e-cigarette prevention campaigns revealed how opposition reflects distinct platform architectures. Twitter opposition was highly coordinated and amplified by commercial and advocacy accounts, especially during regional campaigns. TikTok opposition was decentralized and humor-driven, aligning with the platform's entertainment-oriented algorithm. The findings strengthen health communication by introducing a framework for evaluating platform-specific vulnerabilities and informing evidence-based campaign design. On Twitter, effective countermeasures may require real-time monitoring of social media discourse to support rapid responses to coordinated opposition. On TikTok, leveraging creator partnerships and remix-friendly content may help public health messages compete with entertainment-dominated discourse. Consistent hashtag use can strengthen engagement by minimizing the fragmentation of content visibility, and credible health sources should increasingly reinforce prevention narratives on both platforms. Greater platform accountability and transparency are needed to ensure that prevention content is not systematically deprioritized by algorithms relative to commercial promotion.

Indexed as

Electronic Nicotine Delivery SystemsHealth PromotionSocial MediaVapingAdolescentDigital MediaHumansMedia ExposureRetrospective StudiesUnited Statese-cigaretteshealth campaignshealth communicationpublic healthsocial mediatobacco industry

Identifiers

PMID41886744
PMCPMC13021103

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.