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Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 J+Med+Internet+Res 2020 ; 22 (10): e22624 Nephropedia Template TP
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Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study #MMPMID33006937
Chandrasekaran R; Mehta V; Valkunde T; Moustakas E
J Med Internet Res 2020[Oct]; 22 (10): e22624 PMID33006937show ga
BACKGROUND: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. OBJECTIVE: The aims of this study were to examine key themes and topics of English-language COVID-19-related tweets posted by individuals and to explore the trends and variations in how the COVID-19-related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. METHODS: Building on the emergent stream of studies examining COVID-19-related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19-related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. RESULTS: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19-related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. CONCLUSIONS: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.
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J+Med+Internet+Res 2020 ; 22 (10): e22624
