Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Abstract

AbstractObjectiveTo mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community.Materials and methodsWe retrieved tweets using COVID-19-related keywords, and performed semi-automatic 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 (UMLS), and compared the distributions to those reported in early studies from clinical settings.ResultsWe 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.ConclusionThe spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.