Using health belief model and social media analytics to develop insights from hospital-generated twitter messaging and community responses on the COVID-19 pandemic

IF 7.4 3区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE Journal of Enterprise Information Management Pub Date : 2023-08-15 DOI:10.1108/jeim-06-2021-0267
Xin Tian, Wu He, Yuming He, Steve Albert, Michael Howard
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Abstract

PurposeThis 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/approachThis 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).FindingsThe 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 implicationsFor 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/valueThe 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.
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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大流行的反应中获得见解
目的本研究旨在研究不同的医院如何利用社交媒体与他们服务的社区交流有关新冠肺炎的风险信息,以及医院的社交媒体信息(公司生成的内容和当地社区的反应(用户生成的内容))如何随着新冠肺炎疫情的发展而演变。设计/方法论/方法本研究提出了一个特定于健康护理的社交媒体分析框架,并研究了2019年11月至2020年11月期间发布的68136条推文,这些推文来自地理位置不同的十家领先医院关于新冠肺炎的社交媒体信息,以及通过使用社交媒体分析技术和健康信念模型(HBM)的公众反应一些HBM变量与新冠肺炎疫情进展之间的相关性。这些发现为医院在疫情期间的风险沟通、决策、疫情意识和教育活动以及社交媒体信息策略提供了可操作的见解,并帮助公众为未来疫情的信息寻求做好更多准备。实际意义对于医院来说,研究结果为风险沟通从业者提供了有价值的见解,并为医院或卫生机构在疫情期间管理危机沟通的方式提供了信息。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。独创性/价值该研究证明了社交媒体分析和健康行为模型(如HBM)在确定重要和有用的数据和知识方面的作用,这些数据和知识用于大流行期间的公共卫生风险沟通、应急响应和规划。
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来源期刊
CiteScore
14.80
自引率
6.20%
发文量
30
期刊介绍: The Journal of Enterprise Information Management (JEIM) is a significant contributor to the normative literature, offering both conceptual and practical insights supported by innovative discoveries that enrich the existing body of knowledge. Within its pages, JEIM presents research findings sourced from globally renowned experts. These contributions encompass scholarly examinations of cutting-edge theories and practices originating from leading research institutions. Additionally, the journal features inputs from senior business executives and consultants, who share their insights gleaned from specific enterprise case studies. Through these reports, readers benefit from a comparative analysis of different environmental contexts, facilitating valuable learning experiences. JEIM's distinctive blend of theoretical analysis and practical application fosters comprehensive discussions on commercial discoveries. This approach enhances the audience's comprehension of contemporary, applied, and rigorous information management practices, which extend across entire enterprises and their intricate supply chains.
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