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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. 

With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge, to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.