  A rapidly evolving situation such as the COVID-19 pandemic is a significant
challenge for AI/ML models because of its unpredictability. %The most reliable
indicator of the pandemic spreading has been the number of test positive cases.
However, the tests are both incomplete (due to untested asymptomatic cases) and
late (due the lag from the initial contact event, worsening symptoms, and test
results). Social media can complement physical test data due to faster and
higher coverage, but they present a different challenge: significant amounts of
noise, misinformation and disinformation. We believe that social media can
become good indicators of pandemic, provided two conditions are met. The first
(True Novelty) is the capture of new, previously unknown, information from
unpredictably evolving situations. The second (Fact vs. Fiction) is the
distinction of verifiable facts from misinformation and disinformation. Social
media information that satisfy those two conditions are called live knowledge.
We apply evidence-based knowledge acquisition (EBKA) approach to collect,
filter, and update live knowledge through the integration of social media
sources with authoritative sources. Although limited in quantity, the reliable
training data from authoritative sources enable the filtering of misinformation
as well as capturing truly new information. We describe the EDNA/LITMUS tools
that implement EBKA, integrating social media such as Twitter and Facebook with
authoritative sources such as WHO and CDC, creating and updating live knowledge
on the COVID-19 pandemic.
