Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jun 30, 2020
Open Peer Review Period: Jun 30, 2020 - Jul 8, 2020
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer-review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer-Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
A text-mining analysis of public perceptions and topic modeling during the COVID-19 pandemic using Twitter data
Coronavirus disease (COVID-19) is a scientifically and medically novel disease that is not fully understood as it needs to be consistently and deeply studied. In the past, research on the COVID-19 outbreak was only able to predict quantity data such as the number of outbreaks, but not infoveillance data.
This study aims to understand public perceptions on the trends of the COVID-19 pandemic and uncover meaningful themes of concern posted by Twitter users during the pandemic throughout the world.
Data mining on Twitter was conducted to collect a total of 107,990 tweets between December 13 and March 9, 2020. The analysis included time series, sentiment analysis and topic modeling to identify the most common topics in the tweets as well as to categorize clusters and find themes from keyword analysis.
The results indicate three main aspects of public awareness and concerns regarding the COVID-19 pandemic. Firstly, the study indicated the trend of the spread and symptoms of COVID-19, which was divided into three stages. Secondly, the results of the sentiment analysis and emotional tendency showed that the people had a negative outlook toward COVID-19. Thirdly, topic modeling and themes relating to COVID-19 and the outbreak were divided into three categories, including (1) emergency of COVID-19 impact, (2) the epidemic situation and how to control it, and (3) news and social media reporting on the epidemic.
Sentiment analysis and topic modeling can produce useful information about the trend of COVID-19 pandemic and alternative perspectives to investigate the COVID-19 crisis which has created considerable public awareness around the world. This finding shows that Twitter is a good communication channel for understanding both public concern and awareness about COVID-19 disease. These findings can help health departments to communicate information as to what the public thinks about the disease.
Request queued. Please wait while the file is being generated. It may take some time.
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.