Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource
| Autor: | Mohammed Ali Al-Garadi, Yuan-Chi Yang, Abeed Sarker, Sahithi Lakamana, Angel Xie, Whitney Hogg-Bremer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
2019-20 coronavirus outbreak 020205 medical informatics Coronavirus disease 2019 (COVID-19) social media Pneumonia Viral Anosmia communicable diseases Health Informatics Clinical settings text mining 02 engineering and technology Virus diseases Betacoronavirus 03 medical and health sciences 0302 clinical medicine Research community Internal medicine virus diseases 0202 electrical engineering electronic engineering information engineering medicine Data Mining Humans Social media 030212 general & internal medicine natural language processing Pandemics SARS-CoV-2 business.industry COVID-19 Ageusia Mild symptoms Self Report Symptom Assessment medicine.symptom Coronavirus Infections Brief Communications business |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA Journal of the American Medical Informatics Association |
| DOI: | 10.1101/2020.04.16.20067421 |
| Popis: | ObjectiveTo 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 and MethodsWe 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.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. |
| Databáze: | OpenAIRE |
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