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. 2020 Aug 1;27(8):1310-1315.
doi: 10.1093/jamia/ocaa116.

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

Abeed Sarker  1 , Sahithi Lakamana  1 , Whitney Hogg-Bremer  1 , Angel Xie  1 , Mohammed Ali Al-Garadi  1 , Yuan-Chi Yang  1
Affiliations

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

Abeed Sarker et al. J Am Med Inform Assoc. .

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.

Keywords: communicable diseases; natural language processing; social media; text mining; virus diseases.

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Figures

Figure 1.
Figure 1.
Timeline (not to scale) of first reports of each symptom by the Twitter cohort.
Figure 2.
Figure 2.
Distribution of the number of symptoms reported by the Twitter cohort.
See this image and copyright information in PMC

References

    1. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-re... Accessed April 12, 2020
    1. Sansa NA. Effects of the COVID-19 Pandemic on the World Population: Lessons to Adopt from Past Years Global Pandemics (April 1, 2020). https://ssrn.com/abstract=3565645 or 10.2139/ssrn.3565645. - DOI
    1. COVID-19 Map - Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html Accessed April 12, 2020
    1. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382 (18): 1708–20. - PMC - PubMed
    1. Chen J, Qi T, Liu L, et al. Clinical progression of patients with COVID-19 in Shanghai. China. J Infect 2020; doi : 10.1016/j.jinf.2020.03.004 - PMC - PubMed

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