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
. 2021 Oct;27(5):e12986.
doi: 10.1111/ijn.12986. Epub 2021 Jun 14.

Exploring experiences of COVID-19-positive individuals from social media posts

Jia-Wen Guo  1 , Shawna M Sisler  1 , Ching-Yu Wang  1 , Andrea S Wallace  1
Affiliations

Exploring experiences of COVID-19-positive individuals from social media posts

Jia-Wen Guo et al. Int J Nurs Pract. 2021 Oct.

Abstract

Aims: This study aimed to explore the experience of individuals who claimed to be COVID-19 positive via their Twitter feeds.

Background: Public social media data are valuable to understanding people's experiences of public health phenomena. To improve care to those with COVID-19, this study explored themes from Twitter feeds, generated by individuals who self-identified as COVID-19 positive.

Design: This study utilized a descriptive design for text analysis for social media data.

Methods: This study analysed social media text retrieved by tweets of individuals in the United States who self-reported being COVID-19 positive and posted on Twitter in English between April 2, 2020, and April 24, 2020. In extracting embedded topics from tweets, we applied topic modelling approach based on latent Dirichlet allocation and visualized the results via LDAvis, a related web-based interactive visualization tool.

Results: Three themes were mined from 721 eligible tweets: (i) recognizing the seriousness of the condition in COVID-19 pandemic; (ii) having symptoms of being COVID-19 positive; and (iii) sharing the journey of being COVID-19 positive.

Conclusion: Leveraging the knowledge and context of study themes, we present experiences that may better reflect patient needs while experiencing COVID-19. The findings offer more descriptive support for public health nursing and other translational public health efforts during a global pandemic.

Keywords: coronavirus; patient experience; public health; resilience; text analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Figures

FIGURE 1
FIGURE 1
Intertopic distance map generated by LDAvis for Topic 1, ‘recognizing the seriousness of the condition in COVID‐19 pandemic’ (e.g., size of red circle presents the proportion of Topic 1 information in the overall texts; each red bar presents a term frequency in Topic 1)
FIGURE 2
FIGURE 2
Intertopic distance map generated by LDAvis for Topic 2, ‘having symptoms of being COVID‐19 positive’ (e.g., size of red circle presents the proportion of Topic 2 information in the overall texts; each red bar presents a term frequency in Topic 2)
FIGURE 3
FIGURE 3
Intertopic distance map generated by LDAvis for Topic 3, ‘sharing the journey of being COVID‐19 positive’ (e.g., size of red circle presents the proportion of Topic 3 information in the overall texts; each red bar presents a term frequency in Topic 3)
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Blei, D. M. , Ng, A. Y. , & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. 10.5555/944919.944937 - DOI
    1. Bo, H. X. , Li, W. , Yang, Y. , Wang, Y. , Zhang, Q. , Cheung, T. , Wu, X. , & Xiang, Y. T. (2021). Posttraumatic stress symptoms and attitude toward crisis mental health services among clinically stable patients with COVID‐19 in China. Psychological Medicine, 51(6), 1052–1053. 10.1017/s0033291720000999 - DOI - PMC - PubMed
    1. Cao, J. , Xia, T. , Li, J. , Zhang, Y. , & Tang, S. (2009). A density‐based method for adaptive LDA model selection. Neurocomputing, 72(7), 1775–1781. 10.1016/j.neucom.2008.06.011 - DOI
    1. Centers for Disease Control and Prevention . (2020). Symptoms of coronavirus. Retrieved May 15 from https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
    1. Cuello‐Garcia, C. , Pérez‐Gaxiola, G. , & van Amelsvoort, L. (2020). Social media can have an impact on how we manage and investigate the COVID‐19 pandemic. Journal of Clinical Epidemiology, 127, 198–201. 10.1016/j.jclinepi.2020.06.028 - DOI - PMC - PubMed