Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2020 Sep 25;15(9):e0239441.
doi: 10.1371/journal.pone.0239441. eCollection 2020.

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

Jia Xue  1   2 , Junxiang Chen  3 , Chen Chen  4 , Chengda Zheng  2 , Sijia Li  5   6 , Tingshao Zhu  5
Affiliations
Observational Study

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

Jia Xue et al. PLoS One. .

Abstract

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

PubMed Disclaimer

Conflict of interest statement

NO authors have competing interests Enter: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Preprocessing data chart.
Fig 2
Fig 2. The number of Tweets under the top 9 hashtags by dates.
Fig 3
Fig 3. Coherence score for the number of topics.
Fig 4
Fig 4. Intertopic distance map.
Fig 5
Fig 5. Emotions trends during the early stages of the COVID-19.
See this image and copyright information in PMC

References

    1. Wu H. The coronavirus and Chinese social media: finger-pointing in the post-truth era; 2020[cited 2020 July 7] [Internet]. Available from: https://theconversation.com/the-coronavirus-and-chinese-social-media-fin...
    1. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS ONE.2010; 5(11): e14118 10.1371/journal.pone.0014118 - DOI - PMC - PubMed
    1. Jones JH, Salathe M. Early assessment of anxiety and behavioral response to novel swine-origin influenza A (H1N1). PLoS ONE. 2009; 4(12): e8032 10.1371/journal.pone.0008032 - DOI - PMC - PubMed
    1. Kim Y, Kim JH. Using photos for public health communication: a computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses. Health Informatics Journal. 2020. January 23 10.1177/1460458219896673 - DOI - PubMed
    1. Signorini A, Polgreen PM, Segre, AM. Using Twitter to estimate H1N1 influenza activity. Proceedings of the 9th Annual Conference of the International Society for Disease Surveillance; 2010 Dec. Emerging Health Threats Journal, 2011. 10.3134/ehtj.10.113 - DOI

Publication types

LinkOut - more resources