A transformer based approach for fighting COVID-19 fake news

• URL: http://arxiv.org/abs/2101.12027v1
• Date: Thu, 28 Jan 2021 14:43:42 GMT
• Title: A transformer based approach for fighting COVID-19 fake news
• Authors: S.M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, and Md. Saiful Islam
• Abstract summary: COVID-19 is the first pandemic in history when humanity is the most technologically advanced. Fake news and misinformation regarding this virus is also available to people and causing some massive problems. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work.
• Score: 0.8793721044482612
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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