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Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.
J Med Internet Res. 2020 10 23; 22(10):e22624.JM

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.

Authors+Show Affiliations

Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.Middlesex University Dubai, Dubai, United Arab Emirates.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33006937

Citation

Chandrasekaran, Ranganathan, et al. "Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study." Journal of Medical Internet Research, vol. 22, no. 10, 2020, pp. e22624.
Chandrasekaran R, Mehta V, Valkunde T, et al. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. J Med Internet Res. 2020;22(10):e22624.
Chandrasekaran, R., Mehta, V., Valkunde, T., & Moustakas, E. (2020). Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. Journal of Medical Internet Research, 22(10), e22624. https://doi.org/10.2196/22624
Chandrasekaran R, et al. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. J Med Internet Res. 2020 10 23;22(10):e22624. PubMed PMID: 33006937.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. AU - Chandrasekaran,Ranganathan, AU - Mehta,Vikalp, AU - Valkunde,Tejali, AU - Moustakas,Evangelos, Y1 - 2020/10/23/ PY - 2020/07/18/received PY - 2020/09/26/accepted PY - 2020/08/26/revised PY - 2020/10/3/pubmed PY - 2020/10/29/medline PY - 2020/10/2/entrez KW - COVID-19 KW - coronavirus KW - disease surveillance KW - infodemic KW - infodemiology KW - infoveillance KW - sentiment analysis KW - social media KW - topic modeling KW - trends KW - twitter SP - e22624 EP - e22624 JF - Journal of medical Internet research JO - J Med Internet Res VL - 22 IS - 10 N2 - 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. SN - 1438-8871 UR - https://news.unboundmedicine.com/medline/citation/33006937/Topics_Trends_and_Sentiments_of_Tweets_About_the_COVID_19_Pandemic:_Temporal_Infoveillance_Study_ DB - PRIME DP - Unbound Medicine ER -