Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32937679&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Mining twitter to explore the emergence of COVID-19 symptoms #MMPMID32937679
Guo JW; Radloff CL; Wawrzynski SE; Cloyes KG
Public Health Nurs 2020[Nov]; 37 (6): 934-940 PMID32937679show ga
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.
free
free
free
Public+Health+Nurs 2020 ; 37 (6): 934-940
