Evidence map›Paper›PMID 33006937›Full record

ArticleJournal of medical Internet research2020

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

Ranganathan Chandrasekaran, Vikalp Mehta, Tejali Valkunde, Evangelos Moustakas

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 1 pooled it
67.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

93 citing papers in PubMed, 1 synthesis or guideline pooled it, 239 citations in OpenAlex.

  1. Pooled it
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  10. Mapping the Mpox discourse: A network and sentiment analysis.Exploratory research in clinical and social pharmacy · 2024
    Article
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  14. COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024
    Review
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Ranganathan ChandrasekaranDepartment of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0003-2001-578X
Vikalp MehtaDepartment of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-5873-4475
Tejali ValkundeDepartment of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-2019-1081
Evangelos MoustakasMiddlesex University Dubai, Dubai, United Arab Emirates.ORCID 0000-0002-2671-9035
University of Illinois Chicago · USMiddlesex University Dubai · AE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith 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.

objectiveThe 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.

methodsBuilding 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.

resultsTopic 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.

conclusionsIdentification 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.

Indexed as

Health Knowledge, Attitudes, PracticeBetacoronavirusCoronavirus InfectionsCOVID-19Disease OutbreaksHumansPandemicsPneumonia, ViralSARS-CoV-2Social MediacoronavirusCOVID-19disease surveillanceinfodemicinfodemiologyinfoveillancesentiment analysissocial mediatopic modelingtrendstwitter

Identifiers

PMID33006937
PMCPMC7588259
OpenAlexW3088268279

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.