Purpose

This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.

Design/methodology/approach

This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).

Findings

The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.

Practical implications

For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.

Originality/value

The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

中文翻译：

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使用健康信念模型和社交媒体分析，从医院生成的 Twitter 消息和社区对 COVID-19 大流行的反应中得出见解

目的

本研究旨在探讨不同医院如何利用社交媒体与其服务的社区传达有关 COVID-19 的风险信息，以及医院的社交媒体消息传递（公司生成的内容和当地社区的反应（用户生成的内容））如何随着疫情的发展而演变。 COVID-19 疫情进展。

设计/方法论/途径

这项研究提出了一个针对医疗保健的社交媒体分析框架，并使用社交媒体分析技术和研究了​​ 2019 年 11 月至 2020 年 11 月期间来自不同地理位置的 10 家领先医院发布的关于 COVID-19 的社交媒体消息和公众反应的 68,136 条推文。健康信念模型（HBM）。

发现

该研究发现一些 HBM 变量与 COVID-19 爆发进展之间存在相关性。研究结果为医院在大流行期间的风险沟通、决策、大流行意识和教育活动以及社交媒体消息传递策略方面提供了可行的见解，并帮助公众为未来大流行的情况下寻求信息做好更充分的准备。

实际影响

对于医院而言，研究结果为风险沟通从业者提供了宝贵的见解，并为医院或卫生机构在大流行期间管理危机沟通的方式提供了参考。对于患者和当地社区成员，建议他们查看当地医院的社交媒体网站以获取更新和建议。

原创性/价值

该研究展示了社交媒体分析和健康行为模型（例如 HBM）在识别重要且有用的数据和知识方面的作用，以用于大流行期间的公共卫生风险沟通、应急响应和规划。