Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology

TL;DR

This paper explores using Description Logics reasoning to automatically detect inconsistencies in Covid-19 related information, distinguishing trusted from untrusted sources by analyzing natural language claims through ontology-based reasoning.

Contribution

It introduces a novel approach combining natural language processing and Description Logics reasoning to identify misinformation about Covid-19.

Findings

Effective detection of inconsistent Covid-19 claims

Automated reasoning distinguishes trusted from untrusted sources

Demonstrates feasibility of ontology-based misinformation detection

Abstract

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.