Literature DB >> 27742923

Geographic variations in electronic cigarette advertisements on Twitter in the United States.

Hongying Dai1,2,3, Michael J Deem4,5, Jianqiang Hao6.   

Abstract

OBJECTIVES: Studies have identified a proliferation of e-cigarette advertisements on Twitter. We investigate whether the prevalence of e-cigarette related advertising is associated with state tobacco regulations after taking socio-economic characteristics into account.
METHODS: We collected e-cigarette related tweets from July 23 to October 14, 2015 (n = 757,167) on Twitter. State regulations and smoking prevalence were provided by the Campaign for Tobacco-Free Kids program. The socio-economic data were provided by the American Community Survey.
RESULTS: The number of commercial tweets was 319,041/day with a high potential reach (830,495,700/day). The prevalence of commercial tweets varied significantly by US state. The higher prevalence of e-cigarette advertising was associated with states with better tobacco control impact (r = 0.54, p < 0.0001) and lower youth smoking prevalence (r = -0.39, p = 0.005). In the multivariate analysis, state tobacco control impact is significantly associated with the prevalence of commercial tweets (β = 0.03 ± 0.01, p = 0.02).
CONCLUSIONS: Policies at both the federal and state levels are needed to regulate the content of commercial tweets and mitigate the negative effect of social media advertisements.

Entities:  

Keywords:  Commercial; E-cigarette; Prevalence; Socio-economic factors; State regulation

Mesh:

Year:  2016        PMID: 27742923     DOI: 10.1007/s00038-016-0906-9

Source DB:  PubMed          Journal:  Int J Public Health        ISSN: 1661-8556            Impact factor:   3.380


  28 in total

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2.  Public support for selected e-cigarette regulations and associations with overall information exposure and contradictory information exposure about e-cigarettes: Findings from a national survey of U.S. adults.

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3.  Dual use of electronic and tobacco cigarettes among adolescents: a cross-sectional study in Poland.

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Journal:  Int J Public Health       Date:  2015-10-31       Impact factor: 3.380

4.  E-Cigarette Market Trends in Traditional U.S. Retail Channels, 2012-2013.

Authors:  Daniel P Giovenco; David Hammond; Catherine G Corey; Bridget K Ambrose; Cristine D Delnevo
Journal:  Nicotine Tob Res       Date:  2014-12-26       Impact factor: 4.244

5.  Exposure to Advertisements and Susceptibility to Electronic Cigarette Use Among Youth.

Authors:  Hongying Dai; Jianqiang Hao
Journal:  J Adolesc Health       Date:  2016-08-12       Impact factor: 5.012

6.  Price-related promotions for tobacco products on Twitter.

Authors:  Catherine L Jo; Rachel Kornfield; Yoonsang Kim; Sherry Emery; Kurt M Ribisl
Journal:  Tob Control       Date:  2015-06-30       Impact factor: 7.552

7.  Vital Signs: Exposure to Electronic Cigarette Advertising Among Middle School and High School Students - United States, 2014.

Authors:  Tushar Singh; Kristy Marynak; René A Arrazola; Shanna Cox; Italia V Rolle; Brian A King
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2016-01-08       Impact factor: 17.586

8.  Sociodemographic correlates of self-reported exposure to e-cigarette communications and its association with public support for smoke-free and vape-free policies: results from a national survey of US adults.

Authors:  Andy S L Tan; Cabral A Bigman; Ashley Sanders-Jackson
Journal:  Tob Control       Date:  2014-07-11       Impact factor: 7.552

9.  Electronic cigarette use among Korean adults.

Authors:  Jung Ah Lee; Sun Hee Kim; Hong-Jun Cho
Journal:  Int J Public Health       Date:  2015-11-13       Impact factor: 3.380

Review 10.  Impact of tobacco control interventions on socioeconomic inequalities in smoking: review of the evidence.

Authors:  Sarah Hill; Amanda Amos; David Clifford; Stephen Platt
Journal:  Tob Control       Date:  2013-09-17       Impact factor: 7.552

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  5 in total

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3.  Using Social Media to Target Cancer Prevention in Young Adults: Viewpoint.

Authors:  Urmimala Sarkar; Gem M Le; Courtney R Lyles; Danielle Ramo; Eleni Linos; Kirsten Bibbins-Domingo
Journal:  J Med Internet Res       Date:  2018-06-05       Impact factor: 5.428

4.  Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data.

Authors:  Henna Budhwani; Ruoyan Sun
Journal:  J Med Internet Res       Date:  2020-05-06       Impact factor: 5.428

5.  Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.

Authors:  Sakun Boon-Itt; Yukolpat Skunkan
Journal:  JMIR Public Health Surveill       Date:  2020-11-11
  5 in total

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