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Cited by 244

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence

Open access
Hung, Man, Lauren, Evelyn, Hon, Eric S, Birmingham, Wendy C, Xu, Julie, Su, Sharon, Hon, Shirley D, Park, Jungweon, Dang, Peter, Lipsky, Martin S·2020·Journal of Medical Internet Research
DOI 10.2196/22590
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Abstract

Background: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.

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Metadata

Authors
Hung, M., Lauren, E., Hon, E. S., Birmingham, W. C., Xu, J., Su, S., Hon, S. D., Park, J., Dang, P., & Lipsky, M. S.
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2020-08-18
Volume
22
Issue
8
Pages
e22590
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/22590
OpenAlex
W3047045277
Cited by
244
References
10
FWCI
17.24
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence. https://app.cochat.ai/papers/10.2196/22590
The paper
Hung, M., Lauren, E., Hon, E. S., Birmingham, W. C., Xu, J., Su, S., Hon, S. D., Park, J., Dang, P., & Lipsky, M. S. (2020). Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence. Journal of Medical Internet Research. https://doi.org/10.2196/22590

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