{"title":"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。","authors":"Zachary Tan, Anwitaman Datta","doi":"10.1007/s12553-023-00734-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Data: </strong>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.</p><p><strong>Methodology: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 2","pages":"301-326"},"PeriodicalIF":3.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942624/pdf/","citationCount":"1","resultStr":"{\"title\":\"The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.\",\"authors\":\"Zachary Tan, Anwitaman Datta\",\"doi\":\"10.1007/s12553-023-00734-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Data: </strong>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.</p><p><strong>Methodology: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":12941,\"journal\":{\"name\":\"Health and Technology\",\"volume\":\"13 2\",\"pages\":\"301-326\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942624/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s12553-023-00734-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12553-023-00734-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.
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.
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.