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