Jordan Alexis, Park Albert
Department of Software and Information Systems, UNC Charlotte, Charlotte, NC, United States.
JMIR AI. 2024 Jun 3;3:e54501. doi: 10.2196/54501.
The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance.
In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience.
We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis.
We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building.
The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.
新冠疫情对全球造成了毁灭性影响。在美国，新冠确诊病例超过9800万例，死亡病例超过100万例。新冠感染的一个后果是出现了新冠后状况（PCC）。患有这种综合征的人，通俗地称为“长期新冠患者”，会经历影响其生活质量的症状。PCC的根本原因和有效治疗方法仍然未知。许多长期新冠患者转向社交媒体寻求支持和指导。
在本研究中，我们试图通过调查社交媒体上关于长期新冠患者的讨论内容以及这些信息的被认知方式，来更好地了解长期新冠患者的经历。我们具体调查了以下方面：（1）所讨论症状的范围；（2）长期新冠患者信息的被认知方式；（3）长期新冠患者可获得的信息支持和情感支持；（4）观众与创作者之间的讨论。由于YouTube的受欢迎程度和广泛的受众群体，我们选择它作为数据来源。
我们系统地收集了来自3种不同类型内容创作者的数据：医学来源、新闻来源和长期新冠患者。为了从计算机角度理解视频内容和观众反应，我们使用了Biterm，一种专门为短文本创建的主题建模算法，来分析视频转录片段和评论区的所有顶级评论。为了验证我们关于观众反应的研究结果，我们使用情感感知词典和情感推理器对每种类型内容创作者的评论进行情感分析。我们将评论分为正面和负面类别，并使用Biterm为这些类别生成主题。然后，我们手动将生成的主题归为更广泛的主题以便进行分析。
我们将所有来源的生成主题整理成28个主题。医学来源转录主题的例子有通俗易懂的解释和生物学解释。新闻来源转录主题的例子有负面经历和应对长期状况。长期新冠患者转录主题的两个例子是自行采取治疗措施和日常生活的变化。新闻来源收到的负面评论占比更大。这些负面评论的一些主题包括错误信息和虚假信息以及医疗保健系统问题。同样，长期新冠患者的负面评论也被整理成几个主题，包括对医疗保健系统的失望和需要更多关注。相比之下，医学来源的正面评论涵盖了诸如对有用内容的赞赏和有用信息的交流等主题。除了这个主题外，长期新冠患者评论中发现的一个正面主题是社区建设。
本研究结果可以帮助公共卫生机构、政策制定者、组织和健康研究人员了解与PCC相关的症状和经历。它们还可以帮助这些机构制定有关PCC的沟通策略。
