An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data

Lin Miao, Mark Last, Marina Litvak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With millions of documented recoveries from COVID-19 worldwide, various long-term sequelae have been observed in a large group of survivors. This paper is aimed at systematically analyzing user-generated conversations on Twitter that are related to long-term COVID symptoms for a better understanding of the Long COVID health consequences. Using an interactive information extraction tool built especially for this purpose, we extracted key information from the relevant tweets and analyzed the user-reported Long COVID symptoms with respect to their demographic and geographical characteristics. The results of our analysis are expected to improve the public awareness on long-term COVID-19 sequelae and provide important insights to public health authorities.

Original languageEnglish
Title of host publicationWIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop
EditorsEstevam Hruschka, Tom Mitchell, Dunja Mladenic, Marko Grobelnik, Nikita Bhutani
PublisherAssociation for Computational Linguistics (ACL)
Pages10-19
Number of pages10
ISBN (Electronic)9781955917537
StatePublished - 1 Jan 2022
Event2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022 - Dublin, Ireland
Duration: 27 May 2022 → …

Publication series

NameWIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop

Conference

Conference2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022
Country/TerritoryIreland
CityDublin
Period27/05/22 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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