Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168992
Title: The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study
Authors: Tan, Zachary
Datta, Anwitaman
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Tan, Z. & Datta, A. (2023). The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study. Health and Technology, 13(2), 301-326. https://dx.doi.org/10.1007/s12553-023-00734-6
Journal: Health and Technology
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.
URI: https://hdl.handle.net/10356/168992
ISSN: 2190-7188
DOI: 10.1007/s12553-023-00734-6
Schools: School of Computer Science and Engineering 
Rights: © The Author(s) under exclusive licence to International Union for Physical and Engineering Sciences in Medicine (IUPESM) 2023. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

80
Updated on Jun 24, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.