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

Lin Miao; Mark Last; Marina Litvak

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

Lin Miao, Mark Last, Marina Litvak

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	American English
Title of host publication	WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop
Editors	Estevam Hruschka, Tom Mitchell, Dunja Mladenic, Marko Grobelnik, Nikita Bhutani
Publisher	Association for Computational Linguistics (ACL)
Pages	10-19
Number of pages	10
ISBN (Electronic)	9781955917537
State	Published - 1 Jan 2022
Event	2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022 - Dublin, Ireland Duration: 27 May 2022 → …

Publication series

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

Conference

Conference	2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022
Country/Territory	Ireland
City	Dublin
Period	27/05/22 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Computer Science Applications
Software

Cite this

Miao, L., Last, M., & Litvak, M. (2022). An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. In E. Hruschka, T. Mitchell, D. Mladenic, M. Grobelnik, & N. Bhutani (Eds.), WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop (pp. 10-19). (WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop). Association for Computational Linguistics (ACL).

Miao, Lin ; Last, Mark ; Litvak, Marina. / An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop. editor / Estevam Hruschka ; Tom Mitchell ; Dunja Mladenic ; Marko Grobelnik ; Nikita Bhutani. Association for Computational Linguistics (ACL), 2022. pp. 10-19 (WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop).

@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",

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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)",

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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",

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Miao, L, Last, M & Litvak, M 2022, An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. in E Hruschka, T Mitchell, D Mladenic, M Grobelnik & N Bhutani (eds), WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop. WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop, Association for Computational Linguistics (ACL), pp. 10-19, 2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022, Dublin, Ireland, 27/05/22.

An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. / Miao, Lin; Last, Mark; Litvak, Marina.
WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop. ed. / Estevam Hruschka; Tom Mitchell; Dunja Mladenic; Marko Grobelnik; Nikita Bhutani. Association for Computational Linguistics (ACL), 2022. p. 10-19 (WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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

A2 - Hruschka, Estevam

A2 - Mitchell, Tom

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A2 - Grobelnik, Marko

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Miao L, Last M, Litvak M. An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. In Hruschka E, Mitchell T, Mladenic D, Grobelnik M, Bhutani N, editors, WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2022. p. 10-19. (WIT 2022 - 2nd WIT-Workshop On Deriving Insights From User-Generated Text, Proceedings of the Workshop).

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

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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