Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.
J Am Med Inform Assoc
; 27(8): 1310-1315, 2020 08 01.
Article
in English
| MEDLINE | ID: covidwho-632174
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
OBJECTIVE:
To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS ANDMETHODS:
We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings.RESULTS:
We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies.CONCLUSION:
The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Self Report
/
Pandemics
/
Social Media
/
Symptom Assessment
Type of study:
Diagnostic study
/
Prognostic study
/
Reviews
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2020
Document Type:
Article
Affiliation country:
Jamia
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