Suppr超能文献

多社交媒体网站上的反吸烟公共卫生信息对普通受众的吸引力:比较分析。

General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis.

机构信息

Department of Public Health & Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, United States.

Institute for Health Promotion and Disease Prevention Research, Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States.

出版信息

JMIR Public Health Surveill. 2021 Feb 19;7(2):e24429. doi: 10.2196/24429.

Abstract

BACKGROUND

Public health organizations have begun to use social media to increase awareness of health harm and positively improve health behavior. Little is known about effective strategies to disseminate health education messages digitally and ultimately achieve optimal audience engagement.

OBJECTIVE

This study aims to assess the difference in audience engagement with identical antismoking health messages on three social media sites (Twitter, Facebook, and Instagram) and with a referring link to a tobacco prevention website cited in these messages. We hypothesized that health messages might not receive the same user engagement on these media, although these messages were identical and distributed at the same time.

METHODS

We measured the effect of health promotion messages on the risk of smoking among users of three social media sites (Twitter, Facebook, and Instagram) and disseminated 1275 health messages between April 19 and July 12, 2017 (85 days). The identical messages were distributed at the same time and as organic (unpaid) and advertised (paid) messages, each including a link to an educational website with more information about the topic. Outcome measures included message engagement (ie, the click-through rate [CTR] of the social media messages) and educational website engagement (ie, the CTR on the educational website [wCTR]). To analyze the data and model relationships, we used mixed effects negative binomial regression, z-statistic, and the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

Comparisons between social media sites showed that CTRs for identical antitobacco health messages differed significantly across social media (P<.001 for all). Instagram showed the statistically significant highest overall mean message engagement (CTR=0.0037; 95% CI 0.0032-0.0042), followed by Facebook (CTR=0.0026; 95% CI 0.0022-0.0030) and Twitter (CTR=0.0015; 95% CI 0.0013-0.0017). Facebook showed the highest as well as the lowest CTR for any individual message. However, the message CTR is not indicative of user engagement with the educational website content. Pairwise comparisons of the social media sites differed with respect to the wCTR (P<.001 for all). Messages on Twitter showed the lowest CTR, but they resulted in the highest level of website engagement (wCTR=0.6308; 95% CI 0.5640-0.6975), followed by Facebook (wCTR=0.2213; 95% CI 0.1932-0.2495) and Instagram (wCTR=0.0334; 95% CI 0.0230-0.0438). We found a statistically significant higher CTR for organic (unpaid) messages (CTR=0.0074; 95% CI 0.0047-0.0100) compared with paid advertisements (CTR=0.0022; 95% CI 0.0017-0.0027; P<.001 and P<.001, respectively).

CONCLUSIONS

Our study provides evidence-based insights to guide the design of health promotion efforts on social media. Future studies should examine the platform-specific impact of psycholinguistic message variations on user engagement, include newer sites such as Snapchat and TikTok, and study the correlation between web-based behavior and real-world health behavior change. The need is urgent in light of increased health-related marketing and misinformation on social media.

摘要

背景

公共卫生组织已开始利用社交媒体提高对健康危害的认识,并积极改善健康行为。但是,对于如何有效地在数字环境中传播健康教育信息,最终实现最佳受众参与度,人们知之甚少。

目的

本研究旨在评估在三个社交媒体网站(Twitter、Facebook 和 Instagram)上以及在这些消息中引用的烟草预防网站上发布相同的反吸烟健康信息时,受众参与度的差异。我们假设尽管这些消息是相同的并且同时发布,但它们可能不会在这些媒体上获得相同的用户参与度。

方法

我们测量了 1275 条健康促进信息在三个社交媒体网站(Twitter、Facebook 和 Instagram)上对用户吸烟风险的影响,并于 2017 年 4 月 19 日至 7 月 12 日(85 天)期间进行了传播。相同的消息是同时以有机(免费)和广告(付费)消息的形式发布的,每个消息都包含一个指向有关该主题的教育网站的链接。结果指标包括消息参与度(即社交媒体消息的点击率 [CTR])和教育网站参与度(即教育网站上的点击率 [wCTR])。为了分析数据并建立关系模型,我们使用混合效应负二项回归、z 统计量和 Hosmer-Lemeshow 拟合优度检验。

结果

社交媒体网站之间的比较表明,相同的抗烟草健康信息的 CTR 在社交媒体之间存在显著差异(所有 P<.001)。Instagram 显示出统计学上最高的总体平均消息参与度(CTR=0.0037;95%CI 0.0032-0.0042),其次是 Facebook(CTR=0.0026;95%CI 0.0022-0.0030)和 Twitter(CTR=0.0015;95%CI 0.0013-0.0017)。Facebook 显示出最高和最低的任何单个消息的 CTR。但是,消息 CTR 并不能说明用户对教育网站内容的参与度。社交媒体网站之间的两两比较在 wCTR 方面存在差异(所有 P<.001)。Twitter 上的消息显示出最低的 CTR,但它们导致了最高水平的网站参与度(wCTR=0.6308;95%CI 0.5640-0.6975),其次是 Facebook(wCTR=0.2213;95%CI 0.1932-0.2495)和 Instagram(wCTR=0.0334;95%CI 0.0230-0.0438)。我们发现与付费广告(CTR=0.0022;95%CI 0.0017-0.0027;P<.001 和 P<.001)相比,有机(免费)消息(CTR=0.0074;95%CI 0.0047-0.0100)的 CTR 具有统计学意义上的更高值。

结论

本研究为指导社交媒体上的健康促进工作提供了基于证据的见解。未来的研究应该检查特定平台的心理语言消息变化对用户参与度的影响,包括 Snapchat 和 TikTok 等较新的网站,并研究基于网络的行为与现实世界健康行为改变之间的相关性。鉴于社交媒体上的健康相关营销和错误信息不断增加,这种需求非常迫切。

相似文献

5
Message Design and Audience Engagement with Tobacco Prevention Posts on Social Media.
J Cancer Educ. 2018 Jun;33(3):668-672. doi: 10.1007/s13187-016-1135-x.
8
Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis.
JMIR Public Health Surveill. 2020 Dec 7;6(4):e20649. doi: 10.2196/20649.

引用本文的文献

1
1-866-QUIT-YES: How the Illinois Tobacco Quitline Reached Priority Audiences Through Mass Media.
Tob Use Insights. 2025 May 8;18:1179173X251335197. doi: 10.1177/1179173X251335197. eCollection 2025.
4
Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.
PLoS One. 2024 May 31;19(5):e0303183. doi: 10.1371/journal.pone.0303183. eCollection 2024.
6
Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.
Nicotine Tob Res. 2024 Feb 15;26(Supplement_1):S43-S48. doi: 10.1093/ntr/ntad189.
9
Exploring motivations for engagement with the Healthy Lunch Box campaign on social media.
Health Promot Int. 2023 Dec 1;38(6). doi: 10.1093/heapro/daad151.
10
Non-monetary narratives motivate businesses to engage with climate change.
Sustain Sci. 2023;18(6):2649-2660. doi: 10.1007/s11625-023-01386-1. Epub 2023 Jul 10.

本文引用的文献

1
Social Media in Public Health: Strategies to Distill, Package, and Disseminate Public Health Research.
J Public Health Manag Pract. 2020 Sep/Oct;26(5):489-492. doi: 10.1097/PHH.0000000000001096.
2
Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter.
Cureus. 2020 Mar 13;12(3):e7255. doi: 10.7759/cureus.7255.
3
Vaccination Discussion among Parents on Social Media: A Content Analysis of Comments on Parenting Blogs.
J Health Commun. 2020 Mar 3;25(3):232-242. doi: 10.1080/10810730.2020.1737761. Epub 2020 Mar 10.
4
How to fight an infodemic.
Lancet. 2020 Feb 29;395(10225):676. doi: 10.1016/S0140-6736(20)30461-X.
5
Characterizing HPV Vaccine Sentiments and Content on Instagram.
Health Educ Behav. 2019 Dec;46(2_suppl):37-48. doi: 10.1177/1090198119859412.
7
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media.
Soc Sci Med. 2019 Nov;240:112552. doi: 10.1016/j.socscimed.2019.112552. Epub 2019 Sep 18.
8
Taking Quantitative Data Analysis Out of the Positivist Era: Calling for Theory-Driven Data-Informed Analysis.
Health Educ Behav. 2019 Aug;46(4):537-540. doi: 10.1177/1090198119853536. Epub 2019 Jun 19.
10
Social Media Intervention Design: Applying an Affordances Framework.
J Med Internet Res. 2019 Mar 26;21(3):e11014. doi: 10.2196/11014.

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