@inproceedings{85d1da1d9bda4a16a7316062b91bcc03,
title = "An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data",
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.",
author = "Lin Miao and Mark Last and Marina Litvak",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022 ; Conference date: 27-05-2022",
year = "2022",
month = jan,
day = "1",
language = "American English",
series = "WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "10--19",
editor = "Estevam Hruschka and Tom Mitchell and Dunja Mladenic and Marko Grobelnik and Nikita Bhutani",
booktitle = "WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop",
address = "United States",
}
