Evidence map›Paper›PMID 39546341›Full record

ArticleJournal of medical Internet research2024

The Impact of Comment Slant and Comment Tone on Digital Health Communication Among Polarized Publics: A Web-Based Survey Experiment.

Fangcao Lu, Caixie Tu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Fangcao Lu *Department of Applied Social Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China (Hong Kong).ORCID 0000-0003-4479-1177
Caixie Tu *School of Journalism and Communication, Shanghai University, Shanghai, China.ORCID 0000-0003-2210-2094

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPublic attitudes toward health issues are becoming increasingly polarized, as seen in social media comments, which vary from supportive to oppositional and frequently include uncivil language. The combined effects of comment slant and comment tone on health behavior among a polarized public need further examination.

objectiveThis study aims to examine how social media users' prior attitudes toward mask wearing and their exposure to a mask-wearing-promoting post, synchronized with polarized and hostile discussions, affect their compliance with mask wearing.

methodsThe study was a web-based survey experiment with participants recruited from Amazon Mechanical Turk. A total of 522 participants provided consent and completed the study. Participants were assigned to read a fictitious mask-wearing-promoting social media post with either civil anti-mask-wearing comments (130/522, 24.9%), civil pro-mask-wearing comments (129/522, 24.7%), uncivil anti-mask-wearing comments (131/522, 25.1%), or uncivil pro-mask-wearing comments (132/522, 25.3%). Following this, the participants were asked to complete self-assessed questionnaires. The PROCESS macro in SPSS (model 12; IBM Corp) was used to test the 3-way interaction effects between comment slant, comment tone, and prior attitudes on participants' presumed influence from the post and their behavioral intention to comply with mask-wearing.

resultsAnti-mask-wearing comments led social media users to presume less influence about others' acceptance of masks (B=1.49; P<.001; 95% CI 0.98-2.00) and resulted in decreased mask-wearing intention (B=0.07; P=.03; 95% CI 0.01-0.13). Comment tone with incivility also reduced compliance with mask-wearing (B=-0.44; P=.02; 95% CI -0.81 to -0.07). Furthermore, polarized attitudes had a direct impact (B=0.86; P<.001; 95% CI 0.45-1.26) and also interacted with both the slant and tone of comments, influencing mask-wearing intention.

conclusionsPro-mask-wearing comments enhanced presumed influence and compliance of mask-wearing, but incivility in the comments hindered this positive impact. Antimaskers showed increased compliance when they were unable to find civil support for their opinion in the social media environment. The findings suggest the need to correct and moderate uncivil language and misleading information in online comment sections while encouraging the posting of supportive and civil comments. In addition, information literacy programs are needed to prevent the public from being misled by polarized comments.

Indexed as

Social MediaAdultAttitude to HealthFemaleHealth BehaviorHealth CommunicationHumansInternetMaleMasksMiddle AgedPublic OpinionSurveys and QuestionnairesYoung Adultcomments slanthealth complianceincivilityinfluence of presumed influencemask wearingsocial mediaweb survey

Identifiers

PMID39546341
PMCPMC11607566

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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.