| Literature DB >> 31939747 |
Abstract
BACKGROUND: Electronic cigarettes (e-cigarettes) have been widely promoted on the internet, and subsequently, social media has been used as an important informative platform by e-cigarette users. Beliefs and knowledge expressed on social media platforms have largely influenced e-cigarette uptake, the decision to switch from conventional smoking to e-cigarette smoking, and positive and negative connotations associated with e-cigarettes. Despite this, there is a gap in our knowledge of people's perceptions and sentiments on e-cigarettes as depicted on social media platforms.Entities:
Keywords: electronic cigarettes; electronic nicotine delivery systems; internet; review; social media
Mesh:
Year: 2020 PMID: 31939747 PMCID: PMC6996744 DOI: 10.2196/13673
Source DB: PubMed Journal: JMIR Public Health Surveill ISSN: 2369-2960
Figure 1Flowchart of the literature search process. CINAHL: Cumulative Index of Nursing and Allied Health Literature.
Overall sentiment of discussion on electronic cigarette (e-cigarette) use (coded as pro, anti, neutral, mixed, and not applicable).
| Overall sentiment and studies (first author, year) | Details | ||
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| Allem, 2017 [ | Provaping=92%, neutral=6%, anti=2% | |
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| Lazard, 2016 [ | Pro=68%, neutral=32%, anti=0% | |
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| Ayers, 2017 [ | Reasons for using e-cigarette: quitting combustibles (43%), social image (21%), can vape indoors (17%), flavor choices (14%), safe to use (9%), low cost (3%), and favorable order (2%) | |
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| Zhan, 2017 [ | Reddit: pro=60.7% opponents on e-cigarette bans, neutral=29.9%, anti=9.4% proponents on e- cigarette bans | |
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| Kavuluru, 2016 [ | Proponents versus others: mean positive scores (0.92 and 0.79), mean negative scores (0.01 and 0.03) | |
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| van der Tempel, 2016 [ | Attitude: complete sample versus industry-free sample (pro=79% versus 62%, anti=12% versus 17%, neutral=8% versus 21%); Affective content: complete sample versus industry-free sample (pro=46% versus 27%, anti=7% versus 15%) | |
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| Chu, 2015 [ | Pro=61.9%, anti=47.7%, neutral=8.6% | |
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| Harris, 2014 [ | Pro=89.2% opponents of e-cigarette regulation (antipolicy), anti=7.5% proponents of e-cigarette regulation (propolicy), neutral=3.4% unable to tell | |
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| Lee, 2017 [ | —a | |
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| Chu, 2016 [ | — | |
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| Hua, 2013 [ | Anti=80.5% (negative symptoms), pro=19.3% (positive symptoms), neutral=0.02% (neutral) | |
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| Burke-Garcia, 2017 [ | Neutral=88%-90%, pro=6%, anti=4%-5% | |
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| Dai, 2016 [ | Neutral | |
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| Laestadius, 2016 [ | Neutral: presence of social identity or vaping community (81.2%), depiction of e-cigarette=up to 62.4%; pro=48.3% | |
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| Unger, 2016 [ | Neutral=39.24%, pro=34.96%, anti=25.81% | |
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| Glowacki, 2017 [ | United States: anti=54%, pro=28%, neutral=18%; United Kingdom: pro=43%, anti=37%, neutral=19% | |
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| Cole-Lewis, 2015 [ | Initially, pro=71.11%, neutral=16.78%, anti=12.11%, but showed steady decline in positive sentiment from December 2013 | |
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| Sharma, 2017 [ | — | |
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| Wigginton, 2017 [ | — | |
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| Li, 2016 [ | — | |
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| Chen, 2015 [ | — | |
aCumulative percentage not provided.
Methodological evaluation.
| First author, year | Domains | |||||
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| Study questiona | Data collectionb | Data analysisc | Resultsd | Discussione | Funding or sponsorshipf |
| Allem, 2017 [ | DCAg | DCA | DCA | DCA | DCA | DCA |
| Ayers, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Burke-Garcia, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Chu, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Glowacki, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Lee, 2017 [ | DPAh | DCA | DCA | DCA | DCA | DCA |
| Sharma, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Wigginton, 2017 [ | DPA | DCA | DCA | DCA | DPA | DPA |
| Zhan, 2017 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Dai, 2016 [ | DCA | DCA | DCA | DCA | DCA | DNAi |
| Laestadius, 2016 [ | DPA | DCA | DCA | DCA | DCA | DNA |
| Lazard, 2016 [ | DCA | DCA | DCA | DCA | DCA | DNA |
| Li, 2016 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Kavuluru, 2016 [ | DCA | DCA | DCA | DCA | DPA | DCA |
| Unger, 2016 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| van der Tempel, 2016 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Chen, 2015 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Chu, 2015 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Cole-Lewis, 2015 [ | DCA | DCA | DCA | DCA | DCA | DCA |
| Harris, 2014 [ | DCA | DCA | DCA | DCA | DPA | DNA |
| Hua, 2013 [ | DCA | DCA | DPA | DCA | DCA | DCA |
aStudy question: Was the purpose of the study clear and focused?
bData collection: Was the data collection adequately described (eg, search tool, selection manual, search terms, and capture period)?
cData analysis: Was the description of the data analysis clearly described (eg, coding process, analytic techniques, classification, and statistical tests)?
dResults: Were the outcomes specified (eg, domains or measurement of outcomes)?
eDiscussion: Were conclusions supported by results, with limitations taken into consideration?
fFunding or sponsorship: Was the type and sources of support for study mentioned?
gDCA: domain completely addressed.
hDPA: domain partially addressed.
iDNA: domain not addressed.