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Mining twitter to explore the emergence of COVID‐19 symptoms
Author(s) -
Guo JiaWen,
Radloff Christina L.,
Wawrzynski Sarah E.,
Cloyes Kristin G.
Publication year - 2020
Publication title -
public health nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.471
H-Index - 55
eISSN - 1525-1446
pISSN - 0737-1209
DOI - 10.1111/phn.12809
Subject(s) - covid-19 , social media , pandemic , public health , medicine , disease control , identification (biology) , public health surveillance , family medicine , disease , environmental health , infectious disease (medical specialty) , virology , nursing , pathology , computer science , world wide web , botany , outbreak , biology
Background The Centers for Disease Control and Prevention (CDC) in United States initially alerted the public to three COVID‐19 signs and symptoms—fever, dry cough, and shortness of breath. Concurrent social media posts reflected a wider range of symptoms of COVID‐19 besides these three symptoms. Because social media data have a potential application in the early identification novel virus symptoms, this study aimed to explore what symptoms mentioned in COVID‐19‐related social media posts during the early stages of the pandemic. Methods We collected COVID‐19‐related Twitter tweets posted in English language between March 30, 2020 and April 19, 2020 using search terms of COVID‐19 synonyms and three common COVID‐19 symptoms suggested by the CDC in March. Only unique tweets were extracted for analysis of symptom terms. Results A total of 36 symptoms were extracted from 30,732 unique tweets. All the symptoms suggested by the CDC for COVID‐19 screening in March, April, and May were mentioned in tweets posted during the early stages of the pandemic. Discussion The findings of this study revealed that many COVID‐19‐related symptoms mentioned in Twitter tweets earlier than the announcement by the CDC. Monitoring social media data is a promising approach to public health surveillance.