Evidence map›Paper›PMID 42237270›Full record

ArticleBMC public health2026

Spatiotemporal analysis of electronic cigarette discussion on Twitter/X using natural language processing.

Zidian Xie, Jiamu Tang, Dongmei Li

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

5 · Who and what money

Authors and funding

3 authors.

Zidian XieClinical and Translational Science Institute, University of Rochester Medicine, The United State of America, 265 Crittenden Boulevard CU, Rochester, NY, 420708, 14642-0708, USA. zidian_xie@urmc.rochester.edu.
Jiamu TangGoergen Institute for Data Science and Artificial Intelligence, University of Rochester, Rochester, NY, USA.
Dongmei LiClinical and Translational Science Institute, University of Rochester Medicine, The United State of America, 265 Crittenden Boulevard CU, Rochester, NY, 420708, 14642-0708, USA.

Funding

University of Rochester CTSA HubUM1TR005451 · NCATS · UNIVERSITY OF ROCHESTER · PI Karen M. Wilson, Martin S Zand · 2025 to 2026
$7.0M
Artificial Intelligence for effective communication to promote vaping cessation on social mediaR01CA285482 · NCI · UNIVERSITY OF ROCHESTER · PI Dongmei Li · 2024 to 2026
$1.7M
NCATS NIH HHS UM1 TR005451NCI NIH HHS R01 CA285482
6 · The paper itself

Abstract

backgroundElectronic cigarettes (e-cigarettes) have become popular in recent years, particularly among the youth and young adults. This study aims to examine the spatiotemporal patterns of online discussion of e-cigarettes on Twitter/X.

methodsThrough the Twitter API (Application Programming Interface), over 3 million e-cigarette-related tweets were collected from March 11, 2021, to March 14, 2023, using related keywords, such as "e-cigarette" and "vaping". After data cleaning (such as removing duplicates and retweets) and filtering, 2,140,439 non-commercial tweets were identified. Two human coders independently hand-coded 300 randomly selected tweets regarding relevance (yes or no), sentiment (positive, negative, or neutral), and whether the Twitter user is a likely e-cigarette user (yes or no). An additional 2,000 randomly selected tweets were single-coded. The labeled 2,300 tweets were used to fine-tune a pre-trained RoBERTa (Robustly Optimized BERT) model, which achieved good performance (F1 scores > 0.7). The Latent Dirichlet Allocation (LDA) method was used to identify the major topics in tweets with either positive or negative sentiment.

resultsWe observed a noticeable increase in the number of e-cigarette-related tweets, especially in the UK and Australia, during the study period. Nearly half of the tweets (49.7%, 1,063,317/2,140,439) were neutral. The proportion of tweets with a positive sentiment toward e-cigarettes was higher than that with a negative sentiment, at 27.0% vs. 23.3%. Except for Australia, in the US and UK, especially Canada, there were more positive tweets than negative ones. There was a rising trend in the proportion of tweets with a negative sentiment in the UK and Australia. Additionally, e-cigarette Twitter users were more likely to hold a positive sentiment toward e-cigarettes than non-users, 41.19% vs. 9.74%. Positive topics framed vaping as a desirable, emotionally driven alternative that supports smoking cessation, whereas negative topics emphasized health risks, youth harm, environmental concerns, and calls to quit despite perceived reduced harm.

conclusionsOnline sentiments of e-cigarettes on Twitter varied over time and across different countries. E-cigarette users and non-users held different sentiments toward e-cigarettes. Findings from this study provide timely monitoring in online discussion of e-cigarettes on social media, offering valuable guidance for future tobacco regulations.

Indexed as

Electronic Nicotine Delivery SystemsNatural Language ProcessingSocial MediaSpatio-Temporal AnalysisVapingHumansDeep learning modelE-cigarettesSentimentSocial mediaTopicTwitter/X

Identifiers

PMID42237270
PMCPMC13450413

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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.