Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Abstract

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.

Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis

“…People might develop positive emotions and self-efficacy in coping with this public health threat. For example, sentiment analysis based on COIVD-19 related tweets from January to May 2020 suggested a reversal of sentiments from negative to positive for topics such as public prevention, government response, impact on healthcare industry, and COVID-19 treatment and recovery (Chandrasekaran et al, 2020)…”

“…Chandrasekaran and colleagues conducted text mining of COVID-19-related English tweets from January to May 2020 to group the main topics and uncover the key trends by examining sentiment (positive or negative) scores. (Chandrasekaran, Mehta, Valkunde, & Moustakas, 2020) Dyer and Kolic developed indicators of public risk perception based on emotion and attention presented in tweets from 12 countries between March and June 2020. Twitter users showed differential sensitivity by country to national COVID-19 death rates.…”

Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach

“…Compared to that, there are very few research that has combined both topic modeling and sentiment analysis to investigate COVID-19 data. Chandrasekaran et al [26] discussed topics, trends, and sentiments of tweets about the COVID-19 pandemic using tools such as LDA and VADER. Xue et al [27] analyzed tweets to understand public discourse and sentiment during COVID-19 pandemic.…”

Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

“…Revealed by the quantity of tweets, the public's most prominent concern was death, with over 22.79% of tweets relating to death-related discussion. Previous infoveillance studies of Twitter data in the early period of COVID-19 found that 4.34% of tweets were about death reporting [ 45 ] and 10.54% of tweets pertained to deaths caused by COVID-19 [ 29 ]. The substantial increase in the death-related discussion with the progression of the pandemic highlighted the urgency of communicating adjusting information to the public, which refers to the information helping them to cope psychologically in threatening situations [ 46 ].…”

“…This echoed past crisis research: in risky environments, the first information that should be conveyed to the public is the information that instructs the public on how to protect themselves in the threatened environments [ 46 ]. It also showed that taking actions to prevent the virus from spreading, such as wearing masks and observance of social distancing orders, is a constant topic of the public from the prepandemic period to the peripandemic period [ 29 , 45 ]. The CDC’s instructions on how to act in the context of the COVID-19 pandemic, such as guidelines for reopening, recommendations on wearing masks, and how to make masks, have successfully attracted the public attention as soon as they were released.…”