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

  1. Abeed Sarker
  2. Sahithi Lakamana
  3. Whitney Hogg-Bremer
  4. Angel Xie
  5. Mohammed Ali Al-Garadi
  6. Yuan-Chi Yang

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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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  1. Version published to 10.1093/jamia/ocaa116
  2. ScreenIT

    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
    SentencesResources
    To perform this, we first created a meta-lexicon by combining MedDRA,11 Consumer Health Vocabulary (CHV),12 and SIDER.
    SIDER
    suggested: (SIDER, RRID:SCR_004321)
    16 In the absence of exact matches, we searched the BioPortal to find the most appropriate mappings.
    BioPortal
    suggested: (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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  3. Version published to 10.1101/2020.04.16.20067421v3 on medRxiv
  4. Version published to 10.1101/2020.04.16.20067421v2 on medRxiv
  5. Version published to 10.1101/2020.04.16.20067421v1 on medRxiv