Transformer based Automatic COVID-19 Fake News Detection System

TL;DR

This paper presents an ensemble transformer-based system combining BERT, ALBERT, and XLNET to detect COVID-19 fake news with high accuracy, aiming to curb misinformation spread during the pandemic.

Contribution

It introduces a novel ensemble approach using multiple transformer models for COVID-19 fake news detection, achieving state-of-the-art performance.

Findings

Achieved 0.9855 F1-score on test data

Ranked 5th among 160 teams in shared task

Demonstrated effectiveness of ensemble transformer models

Abstract

Recent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news. Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles. Quick detection of fake news can reduce the spread of panic and confusion among the public. For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our best approach is based on an ensemble of three transformer models (BERT, ALBERT, and XLNET) to detecting fake news. This model was trained and evaluated in the context of the ConstraintAI 2021 shared task COVID19 Fake News Detection in English. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.