An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public
Sentiment for the Coronavirus Disease 2019 Outbreak
#MMPMID33117854
Medford RJ
; Saleh SN
; Sumarsono A
; Perl TM
; Lehmann CU
Open Forum Infect Dis
2020[Jul]; 7
(7
): ofaa258
PMID33117854
show ga
BACKGROUND: Twitter has been used to track trends and disseminate health
information during viral epidemics. On January 21, 2020, the Centers for Disease
Control and Prevention activated its Emergency Operations Center and the World
Health Organization released its first situation report about coronavirus disease
2019 (COVID-19), sparking significant media attention. How Twitter content and
sentiment evolved in the early stages of the COVID-19 pandemic has not been
described. METHODS: We extracted tweets matching hashtags related to COVID-19
from January 14 to 28, 2020 using Twitter's application programming interface. We
measured themes and frequency of keywords related to infection prevention
practices. We performed a sentiment analysis to identify the sentiment polarity
and predominant emotions in tweets and conducted topic modeling to identify and
explore discussion topics over time. We compared sentiment, emotion, and topics
among the most popular tweets, defined by the number of retweets. RESULTS: We
evaluated 126 049 tweets from 53 196 unique users. The hourly number of
COVID-19-related tweets starkly increased from January 21, 2020 onward.
Approximately half (49.5%) of all tweets expressed fear and approximately 30%
expressed surprise. In the full cohort, the economic and political impact of
COVID-19 was the most commonly discussed topic. When focusing on the most
retweeted tweets, the incidence of fear decreased and topics focused on
quarantine efforts, the outbreak and its transmission, as well as prevention.
CONCLUSIONS: Twitter is a rich medium that can be leveraged to understand public
sentiment in real-time and potentially target individualized public health
messages based on user interest and emotion.
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