Related Experiment Video
Updated: Dec 16, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource
Abeed Sarker1, Sahithi Lakamana1, Whitney Hogg-Bremer1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA.
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.
More Related Videos
07:13Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
Published on: April 9, 2021
09:03Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
Published on: November 7, 2020
Related Concept Videos
Steps in Outbreak Investigation
Factors Affecting Illness
For instance, risk factors are connected to illness,...
Surveys
Controls in Experiments
Statistical Methods for Analyzing Epidemiological Data
Myocarditis II: Clinical Features and Diagnostic Tests