2014-2016年埃博拉疫情期间的健康信息需求和健康寻求行为:Twitter内容分析

Michelle Odlum, Sunmoo Yoon
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引用次数: 0

摘要

导言:在2014-2016年埃博拉疫情等重大疾病暴发期间,为了有效的公众沟通,必须充分评估人群的卫生信息需求。通过对社交媒体数据(如推文)的内容分析,可以有效评估公共卫生信息需求,进而提供适当的卫生信息来满足这些需求。当前研究的目的是通过纵向跟踪,在不同的流行时间点评估有关埃博拉的卫生信息需求。方法:采用自然语言处理方法,对2014年7月至2015年3月期间公众对埃博拉疫情的反应进行分析。研究人员分析了155647条提到埃博拉病毒的推文(唯一推文68736条,转发推文86911条),并用信息图表将其可视化。结果:随着时间的推移,观察到公众对埃博拉相关全球优先事项的恐惧、沮丧和健康信息寻求。我们的纵向内容分析显示,由于持续的健康信息不足,导致恐惧和沮丧,社交媒体有时是一种障碍,而不是支持健康信息需求的工具。讨论:推文内容分析有效评估埃博拉信息需求。我们的研究还展示了Twitter作为一种获取实时数据的方法,以评估持续的信息需求、恐惧和沮丧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Health Information Needs and Health Seeking Behavior During the 2014-2016 Ebola Outbreak: A Twitter Content Analysis.

Health Information Needs and Health Seeking Behavior During the 2014-2016 Ebola Outbreak: A Twitter Content Analysis.

Health Information Needs and Health Seeking Behavior During the 2014-2016 Ebola Outbreak: A Twitter Content Analysis.

Health Information Needs and Health Seeking Behavior During the 2014-2016 Ebola Outbreak: A Twitter Content Analysis.

Introduction: For effective public communication during major disease outbreaks like the 2014-2016 Ebola epidemic, health information needs of the population must be adequately assessed. Through content analysis of social media data, like tweets, public health information needs can be effectively assessed and in turn provide appropriate health information to address such needs. The aim of the current study was to assess health information needs about Ebola, at distinct epidemic time points, through longitudinal tracking.

Methods: Natural language processing was applied to explore public response to Ebola over time from July 2014 to March 2015. A total 155,647 tweets (unique 68,736, retweet 86,911) mentioning Ebola were analyzed and visualized with infographics.

Results: Public fear, frustration, and health information seeking regarding Ebola-related global priorities were observed across time. Our longitudinal content analysis revealed that due to ongoing health information deficiencies, resulting in fear and frustration, social media was at times an impediment and not a vehicle to support health information needs.

Discussion: Content analysis of tweets effectively assessed Ebola information needs. Our study also demonstrates the use of Twitter as a method for capturing real-time data to assess ongoing information needs, fear, and frustration over time.

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