Towards Detecting Fake News Using Natural Language Understanding and Reasoning in Description Logics

Fighting against misinformation and computational propaganda requires integrated efforts from various domains like law or education, but there is also a need for computational tools. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted knowledge and not trusted sources. The proposed method is exemplified on fake news for the new coronavirus. Indeed, in the context of the Covid-19 pandemic, many were quick to spread deceptive information. Since, the not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly"), the natural language text is automatically converted into DLs using the FRED tool. The resulted knowledge graph formalised in Description Logics is merged with the trusted ontologies on Covid-10. Reasoning in Description Logics is then performed with the Racer reasoner, which is responsable to detect inconsistencies within the ontology. When detecting inconsistencies, a "red flag" is raised to signal possible fake news. The reasoner can provide justifications for the detected inconsistency. This availability of justifications is the main advantage compared to approaches based on machine learning, since the system is able to explain its reasoning steps to a human agent. Hence, the approach is a step towards human-centric AI systems. The main challenge remains to improve the technology which automatically translates text into some formal representation.