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The rapid advancement of technology in online communication via social mediaplatforms has led to a prolific rise in the spread of misinformation and fakenews. Fake news is especially rampant in the current COVID-19 pandemic, leadingto people believing in false and potentially harmful claims and stories.Detecting fake news quickly can alleviate the spread of panic, chaos andpotential health hazards. We developed a two stage automated pipeline forCOVID-19 fake news detection using state of the art machine learning models fornatural language processing. The first model leverages a novel fact checkingalgorithm that retrieves the most relevant facts concerning user claims aboutparticular COVID-19 claims. The second model verifies the level of truth in theclaim by computing the textual entailment between the claim and the true factsretrieved from a manually curated COVID-19 dataset. The dataset is based on apublicly available knowledge source consisting of more than 5000 COVID-19 falseclaims and verified explanations, a subset of which was internally annotatedand cross-validated to train and evaluate our models. We evaluate a series ofmodels based on classical text-based features to more contextual Transformerbased models and observe that a model pipeline based on BERT and ALBERT for thetwo stages respectively yields the best results.

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking