Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology
This addresses the spread of deceptive information during the Covid-19 pandemic, but it is incremental as it applies existing reasoning methods to a new domain.
The paper tackled the problem of detecting fake news about Covid-19 by using Description Logics to identify inconsistencies between trusted medical sources and untrusted natural language claims, achieving automated detection through tools like FRED and Racer.
In the context of the Covid-19 pandemic, many were quick to spread deceptive information. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly"). To automatically convert into DLs, I used the FRED converter. Reasoning in Description Logics is then performed with the Racer tool.