Covid-19 fake news detection by using BERT and RoBERTa models - Pavlov, Tashko; Mirceva, Georgina (IEEE, 2022)
We live in a world where COVID-19 news is an everyday occurrence with which we interact. We are receiving that information, either consciously or unconsciously, without fact-checking it. In this regard, it has become an enormous challenge to keep only true COVID-19 news relevant. People are exposed to these stories on a daily basis, and not all of them are true and fact-checked reports on the COVID-19 pandemic, which was the primary reason for our research. We accepted the challenge that fake news is extremely common and that some people take these news as they are. Knowing the true power of the most recent NLP achievements, in this research we focus on detecting fake news regarding COVID-19. Our approach includes using pre-trained BERT and RoBERTa models, which we then fine-tune on real and fake news about the COVID-19 pandemic. By using pre-trained BERT and RoBERTa models on tweet data, we explore their capabilities and compare them to previous research in regard to fine-tuned BERT models for this task in which we achieve better accuracy, recall and f1 score.
Keywords: COVID-19 , fake news , deep learning , transformer models , BERT , RoBERTa