BACKGROUND
In Situation Report #13 and 39 days before declaring COVID-19 a pandemic, the WHO declared a “COVID-19 infodemic”. The volume of coronavirus tweets was far too great for one to find accurate or reliable information. Healthcare workers were flooded with “noise” which drowned the “signal” of valuable COVID-19 information. To combat the infodemic, physicians created healthcare-specific micro-communities to share scientific information with other providers.
OBJECTIVE
Our objective was to eliminate noise and elevate signal tweets related to COVID-19 and provide easy access to the most educational tweets for medical professionals who were searching for information.
METHODS
We analyzed the content of eight physician-created communities and categorized each message in one of five domains. We coded 1) an application programming interface to download tweets and their metadata in JavaScript Object Notation and 2) a reading algorithm using visual basic application in Excel to categorize the content. We superimposed the publication date of each tweet into a timeline of key pandemic events. Finally, we created NephTwitterArchive.com to help healthcare workers find COVID-19-related signal tweets when treating patients.
RESULTS
We collected 21071 tweets from the eight hashtags studied. Only 9051 tweets were considered signal: tweets categorized into both a domain and subdomain. There was a trend towards fewer signal tweets as the pandemic progressed, with a daily median of 22% (IQR 0-42%). The most popular subdomain in Prevention was PPE (2448 signal tweets). In Therapeutics, Hydroxychloroquine/chloroquine wwo Azithromycin and Mechanical Ventilation were the most popular subdomains. During the active Infodemic phase (Days 0 to 49), a total of 2021 searches were completed in NephTwitterArchive.com, which was a 26% increase from the same time period before the pandemic was declared (Days -50 to -1).
CONCLUSIONS
The COVID-19 Infodemic indicates that future endeavors must be undertaken to eliminate noise and elevate signal in all aspects of scientific discourse on Twitter. In the absence of any algorithm-based strategy, healthcare providers will be left with the nearly impossible task of manually finding high-quality tweets from amongst a tidal wave of noise.
CLINICALTRIAL
not applicable