Evidence map›Paper›PMID 36330277›Full record

ArticleTobacco induced diseases2022

Attitudes towards the 'Shisha No Thanks' campaign video: Content analysis of Facebook comments.

Lilian Chan, Ben Harris-Roxas, Becky Freeman, Ross MacKenzie, Lisa Woodland, Blythe J O'Hara

Abstract read
In one paragraph

Article in Tobacco induced diseases, 2022. 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.

Lilian ChanPrevention Research Collaboration, Charles Perkins Centre, University of Sydney, Camperdown, Australia.
Ben Harris-RoxasSchool of Population Health, University of New South Wales, Sydney, Australia.
Becky FreemanPrevention Research Collaboration, Charles Perkins Centre, University of Sydney, Camperdown, Australia.
Ross MacKenzieCentre for Primary Health Care and Equity, University of New South Wales, Sydney, Australia.
Lisa WoodlandNew South Wales Multicultural Health Communication Service, Sydney, Australia.
Blythe J O'HaraPrevention Research Collaboration, Charles Perkins Centre, University of Sydney, Camperdown, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWhile social media are commonly used in public health campaigns, there is a gap in our understanding of what happens after the campaign is seen by the target audience. This study aims to understand how the

methodsA sample of the Facebook comments was extracted, and the study team, which included cultural support workers, developed content categories consistent with the research question. Each comment was then coded by three team members, and only assigned a category if there was agreement by at least two members.

resultsOf the 4990 comments that were sampled, 9.1% (456) accepted the campaign message, 22.9% (1144) rejected the message, 21.8% (1089) were unclear, and 46.1% (2301) contained only tagged names. Of the sample, 2.8% (138) indicated the commenter took on board the campaign message by expressing an intention to stop smoking shisha, or asking a friend to stop smoking shisha. Of the comments that showed rejection of the campaign, the majority were people dismissing the campaign by laughing at it or expressing pro-shisha sentiments.

conclusionsThis study demonstrates that conducting content analyses of social media comments can provide important insight into how a campaign message is received by a social media audience.

Indexed as

campaigncontent analysisshishasocial mediawaterpipe

Identifiers

PMID36330277
PMCPMC9578129

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.