An Ensemble Learning Approach for COVID-19 Fact Verification

ABSTRACT

The rapid development of social media platforms has resulted in a fast-paced spread of misinformation, which is especially common in the COVID-19 pandemic. In the global pandemic, the amount of COVID-19 related fake news generated online becomes enormous, which negatively results in public tension. Moreover, rumours are spread across platforms from different countries in such a global pandemic. Thus, automated fact-checking, which refers to automatically verifying the correctness of a claim, is of great importance. In this paper, we propose and examine ensemble learning approaches that exploit the power of multiple large-scale pre-trained language models. We conduct extensive experiments on traditional approaches, learning-based approaches, and our proposed ensemble methods. We successfully advance state-of-the-art performance by a significant margin. Further, we show that our ensemble method is especially suited to tasks with scarce training data, making it more suitable for many real-world applications. © 2022 IEEE.