Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media
Calton Pu, Abhijit Suprem, and Rodrigo Alves Lima

TL;DR
This paper explores how social media, combined with authoritative sources, can provide real-time, reliable insights into rapidly evolving pandemics by filtering misinformation and capturing new information.
Contribution
It introduces the EBKA approach and EDNA/LITMUS tools for integrating social media with authoritative data to create and update live knowledge during pandemics.
Findings
Effective filtering of misinformation from social media
Capture of new, previously unknown pandemic information
Real-time updating of pandemic knowledge using integrated sources
Abstract
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…
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Taxonomy
TopicsMisinformation and Its Impacts · Data-Driven Disease Surveillance · Anomaly Detection Techniques and Applications
