About: Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends

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About: Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
Creator	Sheldenkar, Anita Yang, Yinping Eysenbach, Gunther Gandhi, Chintan Schulz, Peter Lwin, May Hochheiser, Harry Da Silva, Edson Gupta, Raj Rashid Soron, Tanjir Villegas, Jorge Cesare, Nina Lu, Jiahui Shin, Wonsun Toledo, Marileila
Source	Medline; PMC; WHO
abstract	BACKGROUND: With the World Health Organization’s pandemic declaration and government-initiated actions against coronavirus disease (COVID-19), sentiments surrounding COVID-19 have evolved rapidly. OBJECTIVE: This study aimed to examine worldwide trends of four emotions—fear, anger, sadness, and joy—and the narratives underlying those emotions during the COVID-19 pandemic. METHODS: Over 20 million social media twitter posts made during the early phases of the COVID-19 outbreak from January 28 to April 9, 2020, were collected using “wuhan,” “corona,” “nCov,” and “covid” as search keywords. RESULTS: Public emotions shifted strongly from fear to anger over the course of the pandemic, while sadness and joy also surfaced. Findings from word clouds suggest that fears around shortages of COVID-19 tests and medical supplies became increasingly widespread discussion points. Anger shifted from xenophobia at the beginning of the pandemic to discourse around the stay-at-home notices. Sadness was highlighted by the topics of losing friends and family members, while topics related to joy included words of gratitude and good health. CONCLUSIONS: Overall, global COVID-19 sentiments have shown rapid evolutions within just the span of a few weeks. Findings suggest that emotion-driven collective issues around shared public distress experiences of the COVID-19 pandemic are developing and include large-scale social isolation and the loss of human lives. The steady rise of societal concerns indicated by negative emotions needs to be monitored and controlled by complementing regular crisis communication with strategic public health communication that aims to balance public psychological wellbeing.
has issue date	2020-05-22(xsd:dateTime)
bibo:doi	10.2196/19447
bibo:pmid	32412418
has license	cc-by
schema:url	https://doi.org/10.2196/19447
resource representing a document's title	Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
has PubMed Central identifier	PMC7247466
has PubMed identifier	32412418
schema:publication	JMIR Public Health Surveill
resource representing a document's body	covid:PMC7247466#body_text
is schema:about of	named entity 'TRENDS' named entity 'COVID-19 Pandemic' named entity 'Twitter' named entity 'human lives' named entity 'public health' named entity 'March 12' named entity 'emotion' named entity 'COVID-19' named entity 'negative emotions' named entity 'COVID-19' named entity 'WHO' named entity 'crisis communication' named entity 'public health' named entity 'Wheel of Emotions' named entity 'mental wellbeing' named entity 'WHO' named entity 'Weibo' named entity 'social isolation' named entity 'basic emotions' named entity 'COVID-19' named entity 'basic emotions' named entity 'World Health Organization' named entity 'public health' named entity 'COVID-19' named entity 'application programming interface' named entity 'traumatic experiences' named entity 'COVID-19 pandemic' named entity 'Twitter' named entity 'COVID-19' named entity 'communication patterns' named entity 'Asia' named entity 'general sentiment' named entity 'WHO' named entity 'xenophobia' named entity 'valences' named entity 'emotion' named entity 'social media analytic' named entity 'COVID-19' named entity 'application programming interface' named entity 'China' named entity 'emotional responses' named entity 'emotion' named entity 'tweets' named entity 'valences' named entity 'tweets' named entity 'unigrams' named entity 'negative emotion' named entity 'COVID-19' named entity 'COVID-19 pandemic' named entity 'algorithm' named entity 'Twitter' named entity 'Twitter’s' named entity 'Facebook' named entity 'Ebola' named entity 'COVID-19' named entity 'emotion' named entity 'Negative emotions' named entity 'coronavirus disease' named entity 'social media' named entity 'Twitter' named entity 'Twitter' named entity 'COVID-19' named entity 'Twitter' named entity 'Pandemic' named entity 'Analysis' named entity 'SURROUNDING' named entity 'COVID-19 PANDEMIC' named entity 'ANALYSIS'

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