Evidence map›Paper›PMID 26508089›Full record

ArticleJournal of medical Internet research2015

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

Heather Cole-Lewis, Jillian Pugatch, Amy Sanders, Arun Varghese, Susana Posada, Christopher Yun, Mary Schwarz, Erik Augustson

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 5 pooled it
–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

93 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Overview of Electronic Nicotine Delivery Systems: A Systematic Review.American journal of preventive medicine · 2017
    Pooled it
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  10. Paid Social Media Advertising on Vaping in Australia: A Descriptive Study Using Meta Ad Library.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2025
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  14. Awareness of and support for banning e-cigarettes and heated tobacco products among Mexicans who smoke.Revista panamericana de salud publica = Pan American journal of public health · 2025
    Article
  15. Categorizing E-cigarette-related tweets using BERT topic modeling.Emerging trends in drugs, addictions, and health · 2024
    Article
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33 more citing papers are in PubMed but not listed here.

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

8 authors.

Funding

NCI NIH HHS HHSN261200900022C
6 · The paper itself

Abstract

backgroundElectronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter.

objectiveThe objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data.

methodsA 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends.

resultsThe analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined.

conclusionsEveryday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

Indexed as

Electronic Nicotine Delivery SystemsFemaleHumansInternetPublic OpinionSmoking CessationSocial MediaUnited Statescontent analysise-cigarettessocial mediaTwitter

Identifiers

PMID26508089
PMCPMC4642379

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.