Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 17, 2020
Date Accepted: Aug 3, 2020
Date Submitted to PubMed: Aug 4, 2020
Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence
ABSTRACT
Background:
The COVID-19 pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments and individuals to navigate the pandemic.
Objective:
To examine social media discussion on Twitter related to COVID-19 and to investigate the sentiments towards COVID-19.
Methods:
This study applied machine learning methods to analyze data collected from the Twitter website. Using tweets originating exclusively in the United States and written in English during the one-month period from March 20, 2020 to April 19, 2020, the study examined COVID-19 related discussions. Sentiment analysis was also conducted to determine whether the tweets expressed positive, neutral or negative sentiments, as well as the degree of sentiments.
Results:
There was a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets and 641,381 mentions in tweets during the study timeframe. Sentiment analysis classified 434,254 tweets as positive, 187,042 as neutral and 280,842 as having negative COVID sentiment. The study identified five dominant themes among COVID-19 related tweets: Healthcare 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 five prevalent themes of COVID-19 discussion with sentiments ranging from positive, negative, and neutral. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© 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.