Evidence map›Paper›PMID 29942600›Full record

ArticleDigital health

Mobilizing social media users to become advertisers: Corporate hashtag campaigns as a public health concern.

Linnea I Laestadius, Megan M Wahl

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

2 authors.

Linnea I LaestadiusJoseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, USA.
Megan M WahlJoseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWith the growing popularity of social media, corporate marketers are increasingly launching hashtag campaigns to encourage consumers to create branded user-generated content on their behalves. If successful, these campaigns may expand the reach of harmful marketing messages and capitalize on peer-effects among adolescents. To shed light on these novel corporate campaigns, we performed a case study of the user-generated Instagram content created in response to a hashtag campaign promoting the quick-service restaurant Kentucky Fried Chicken (KFC).

methodsWe performed a content analysis of one week's worth of user-generated Instagram posts created with the hashtag #HowDoYouKFC between 24 April 2015-1 May 2015. Posts were coded to discern: (a) relevance to KFC as a brand and/or food; (b) themes in the post, (c) the content of images/videos, and (d) overall sentiment toward KFC. Posts that were deleted or made private during the study period were removed. Descriptive statistics were calculated to discern trends in post content.

resultsInstagram users created 196 posts with #HowDoYouKFC during the study period. After removing irrelevant and deleted/private posts, analysis of the 128 remaining user-generated posts revealed that 45% of posts were explicitly positive toward KFC and 39% lacked a specific stance or emotion related to KFC. Of the posts, 55% depicted KFC chicken and 65% included depictions of the brand hashtag on food packaging.

conclusionsFindings indicate that corporations are successfully converting individual social media users into positive advertisers for harmful products. Novel efforts are needed to counter corporate user-generated content campaigns.

Indexed as

corporationsfast foodsmarketingSocial media

Identifiers

PMID29942600
PMCPMC6001194

What OpenQuestion holds

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LicenceCC BY-NC
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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.