Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource
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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 and Methods
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.
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SciScore for 10.1101/2020.04.16.20067421: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources To perform this, we first created a meta-lexicon by combining MedDRA,11 Consumer Health Vocabulary (CHV),12 and SIDER. SIDERsuggested: (SIDER, RRID:SCR_004321)16 In the absence of exact matches, we searched the BioPortal to find the most appropriate mappings. BioPortalsuggested: (BioPortal, RRID:SCR_002713)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not …SciScore for 10.1101/2020.04.16.20067421: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources To perform this, we first created a meta-lexicon by combining MedDRA,11 Consumer Health Vocabulary (CHV),12 and SIDER. SIDERsuggested: (SIDER, RRID:SCR_004321)16 In the absence of exact matches, we searched the BioPortal to find the most appropriate mappings. BioPortalsuggested: (BioPortal, RRID:SCR_002713)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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