Suppr超能文献

理解对疫苗持犹豫或拒绝态度的社交媒体影响者的信息和动机。

Understanding the messages and motivation of vaccine hesitant or refusing social media influencers.

机构信息

Division of Population Science, Medical Oncology, Thomas Jefferson University, 834 Chestnut Street, Suite 314, Philadelphia, PA 19107, United States.

NORC at the University of Chicago, 4350 East-West Highway, 8th Floor, Bethesda, MD 20814, United States.

出版信息

Vaccine. 2021 Jan 8;39(2):350-356. doi: 10.1016/j.vaccine.2020.11.058. Epub 2020 Dec 3.

Abstract

BACKGROUND

While anti-vaccine messages on social media have been studied for content, reach, and effectiveness, less is known about those who create and promote the messages. Online influencers, or 'everyday people who are influential within their online social networks', are viewed as trusted voices who are often making similar life decisions as their followers. Therefore, their experiences with and perspectives on health issues can be persuasive.

METHODS

We collaborated with a formal network of online influencers to interview, using a semi-structured interview guide, vaccine hesitant influencer mothers about their views on vaccination; their process for developing health-related social media content; their motivation to promote anti-vaccine messages; and their opinions on current vaccination messaging. Prescreening ensured a diverse sample by race/ethnicity, age, education, number of children, and geographic residence. Interviews occurred by telephone, were audio recorded, and transcribed. Themes were generated independently by two coders using a deductive coding approach.

RESULTS

We interviewed 15 online influencer mothers from across the U.S. (average age 39 years old; all married; 13 Caucasian, 1 African American, 1 Hispanic). In some capacity, 5 of the 15 wrote about vaccination on their blog. Those who chose not to post anti-vaccine content did so for fear of alienating followers or having their platform be the site of combative discourse among readers. When researching their social media posts, the influencers did not trust mainstream sources of health information and relied on alternative sources and search engines.

IMPLICATIONS

This exploratory study interviewed influential mothers who have the ability to spread anti-vaccine messages on social media. While most do not contribute to the anti-vaccine sentiment, understanding the motivation and practices of those that do assists the public health community in better understanding the online vaccination communication environment, leading to more effective messages to counterbalance anti-vaccine content on social media.

摘要

背景

虽然社交媒体上的反疫苗信息已经在内容、传播范围和效果方面进行了研究,但对于发布和推广这些信息的人了解较少。网络影响者,或“在其在线社交网络中具有影响力的普通人”,被视为可信赖的声音,他们经常与粉丝做出类似的生活决策。因此,他们对健康问题的看法和观点可能具有说服力。

方法

我们与一个正式的网络影响者网络合作,对犹豫不决的疫苗接种者的母亲进行采访,使用半结构化访谈指南,了解他们对疫苗接种的看法;他们制作与健康相关的社交媒体内容的过程;他们推广反疫苗信息的动机;以及他们对当前疫苗接种信息的看法。预先筛选确保了种族/民族、年龄、教育程度、子女数量和居住地区的多样性样本。访谈通过电话进行,录音并转录。两位编码员使用演绎编码方法独立生成主题。

结果

我们采访了来自美国各地的 15 位网络影响者母亲(平均年龄 39 岁;均已婚;13 位白种人,1 位非裔美国人,1 位西班牙裔)。其中 5 位在某种程度上在他们的博客上撰写了关于疫苗接种的文章。那些选择不发布反疫苗内容的人这样做是因为担心疏远粉丝,或者担心他们的平台成为读者之间激烈辩论的场所。在研究他们的社交媒体帖子时,这些影响者不信任主流健康信息来源,而是依赖替代来源和搜索引擎。

含义

这项探索性研究采访了具有在社交媒体上传播反疫苗信息能力的有影响力的母亲。虽然大多数人不会助长反疫苗情绪,但了解那些确实如此的人的动机和做法,有助于公共卫生界更好地了解社交媒体上的在线疫苗接种传播环境,从而使更有效的信息能够对抗社交媒体上的反疫苗内容。

相似文献

3
Vaccine Hesitancy and Online Information: The Influence of Digital Networks.疫苗犹豫与网络信息:数字网络的影响。
Health Educ Behav. 2018 Aug;45(4):599-606. doi: 10.1177/1090198117739673. Epub 2017 Dec 21.
4
Semantic network analysis of vaccine sentiment in online social media.在线社交媒体中疫苗情绪的语义网络分析
Vaccine. 2017 Jun 22;35(29):3621-3638. doi: 10.1016/j.vaccine.2017.05.052. Epub 2017 May 27.

引用本文的文献

3
COVID-19 and social media: Beyond polarization.新冠疫情与社交媒体:超越两极分化
PNAS Nexus. 2023 Aug 1;2(8):pgad246. doi: 10.1093/pnasnexus/pgad246. eCollection 2023 Aug.
6
Media Data and Vaccine Hesitancy: Scoping Review.媒体数据与疫苗犹豫:范围综述
JMIR Infodemiology. 2022 Aug 10;2(2):e37300. doi: 10.2196/37300. eCollection 2022 Jul-Dec.
7
Negative COVID-19 Vaccine Information on Twitter: Content Analysis.推特上关于新冠疫苗的负面信息:内容分析
JMIR Infodemiology. 2022 Aug 29;2(2):e38485. doi: 10.2196/38485. eCollection 2022 Jul-Dec.

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验