An Explainable Knowledge Graph Based Machine Learning Model for Fact Checking

Misinformation is a growing threat to the economy, social stability, public health, democracy, and national security. One of the most effective methods to combat misinformation is fact checking.

In this thesis, we propose fact checking methods using NLP and misinformation propagation patterns. The contributions are,

A KG-based fact checking model that uses two separate KGs, one containing true claims and the other, false claims. Additionally, we employ XAI techniques to provide explanations for the model's classification, increasing transparency and user trust.

A propagation-based classifier to complement the above KG-based fact checking model for misinformation detection on Twitter. This model uses temporal, spatial and "infectiousness" properties of misinformation.

A translator program that converts text with slang and non-standard words (SNSW) into standard English for fact checking on Reddit. The translated content is then input into the above KG-based fact checking model, increasing the model's accuracy.