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

TL;DR

This paper presents a two-stage Transformer-based pipeline for detecting COVID-19 fake news by retrieving relevant facts and verifying claims through textual entailment, improving accuracy in misinformation detection.

Contribution

The study introduces a novel two-stage approach combining fact retrieval and textual entailment verification using BERT and ALBERT models for COVID-19 fake news detection.

Findings

Transformer models outperform classical features

BERT and ALBERT pipeline achieves best results

Effective detection of COVID-19 misinformation

Abstract

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.