A transformer based approach for fighting COVID-19 fake news

TL;DR

This paper presents a transformer-based ensemble model that effectively detects COVID-19 related fake news, achieving high accuracy and precision in a competitive challenge.

Contribution

It introduces a stacking ensemble of eight pre-trained transformer models specifically fine-tuned for COVID-19 fake news detection.

Findings

Achieved nearly 98% accuracy on test data

Outperformed baseline models in precision and recall

Demonstrated effectiveness of transformer ensembles for misinformation detection

Abstract

The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work. After extensive experimentation with numerous architectures and techniques, we use eight different transformer-based pre-trained models with additional layers to construct a stacking ensemble classifier and fine-tuned them for our purpose. We achieved 0.979906542 accuracy, 0.979913119 precision, 0.979906542 recall, and 0.979907901 f1-score on the test dataset of the competition.