About: An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the COVID-19 Outbreak

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the COVID-19 Outbreak
Creator	Perl, Trish Lehmann, Christoph Medford, Richard Saleh, Sameh Sumarsono, Andrew Dallas, Hines Blvd, Harry
Source	PMC
abstract	BACKGROUND: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about Coronavirus Disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. METHODS: We extracted tweets matching hashtags related to COVID-19 from January 14(th) to 28(th), 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. RESULTS: We evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19 related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. CONCLUSION: Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.
has issue date	2020-06-30(xsd:dateTime)
bibo:doi	10.1093/ofid/ofaa258
has license	cc-by-nc-nd
sha1sum (hex)	a3bad73497ddb2f845a2d8b490104c4220c88e5c
schema:url	https://doi.org/10.1093/ofid/ofaa258
resource representing a document's title	An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the COVID-19 Outbreak
has PubMed Central identifier	PMC7337776
schema:publication	Open Forum Infect Dis
resource representing a document's body	covid:a3bad73497ddb2f845a2d8b490104c4220c88e5c#body_text
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