The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.

IF 3.1 Q2 MEDICAL INFORMATICS Health and Technology Pub Date : 2023-01-01 DOI:10.1007/s12553-023-00734-6
Zachary Tan, Anwitaman Datta
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引用次数: 1

Abstract

Data: This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021.

Methodology: On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news.

Results: Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year.

Conclusion: Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

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从reddit的r/冠状病毒子版块看Covid-19大流行的第一年:一项探索性研究。
数据:本研究着眼于Reddit的COVID-19社区r/冠状病毒的内容,以捕捉和理解围绕全球大流行的主题和讨论,以及它们在大流行第一年的演变。它研究了356,690份提交(帖子)和9,413,331条与提交相关的评论,对应于2020年1月20日至2021年1月31日期间。方法:在这些数据集上,我们基于词汇情感和由无监督主题建模生成的主题进行了分析。研究发现,在提交的文章中,负面情绪的比例更高,而在评论中,负面情绪与正面情绪的比例相同。确定了更多积极或消极相关的术语。通过对“赞”和“贬”的评估,这项研究还发现了有争议的话题,尤其是“假”或误导性新闻。结果:通过主题建模,从提交中确定了9个不同的主题,从评论中确定了20个主题。总的来说,这项研究清楚地概述了第一年与大流行有关的主要话题和公众情绪。结论:我们的方法为政府和卫生决策者和当局提供了一个宝贵的工具,可以更深入地了解主要的公众关切和态度,这对于理解、设计和实施针对全球大流行的干预措施至关重要。
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来源期刊
Health and Technology
Health and Technology MEDICAL INFORMATICS-
CiteScore
7.10
自引率
0.00%
发文量
83
期刊介绍: Health and Technology is the first truly cross-disciplinary journal on issues related to health technologies addressing all professions relating to health, care and health technology.The journal constitutes an information platform connecting medical technology and informatics with the needs of care, health care professionals and patients. Thus, medical physicists and biomedical/clinical engineers are encouraged to write articles not only for their colleagues, but directed to all other groups of readers as well, and vice versa.By its nature, the journal presents and discusses hot subjects including but not limited to patient safety, patient empowerment, disease surveillance and management, e-health and issues concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. The journal also addresses the medical, financial, social, educational and safety aspects of health technologies as well as health technology assessment and management, including issues such security, efficacy, cost in comparison to the benefit, as well as social, legal and ethical implications.This journal is a communicative source for the health work force (physicians, nurses, medical physicists, clinical engineers, biomedical engineers, hospital engineers, etc.), the ministries of health, hospital management, self-employed doctors, health care providers and regulatory agencies, the medical technology industry, patients'' associations, universities (biomedical and clinical engineering, medical physics, medical informatics, biology, medicine and public health as well as health economics programs), research institutes and professional, scientific and technical organizations.Health and Technology is jointly published by Springer and the IUPESM (International Union for Physical and Engineering Sciences in Medicine) in cooperation with the World Health Organization.
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