Abstract
COVID-19 has caused havoc globally due to its transmission pace among the inhabitants and prolific rise in the number of people contracting the disease worldwide. As a result, the number of people seeking information about the epidemic via Internet media has increased. The impact of the hysteria that has prevailed makes people believe and share everything related to illness without questioning its truthfulness. As a result, it has amplified the misinformation spread on social media networks about the disease. Today, there is an immediate need to restrict disseminating false news, even more than ever before. This paper presents an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification. From the observation, we found that the proposed early fusion-based method outperforms models that work on single embeddings. We also conducted detailed studies using several machine learning and deep learning models to classify misinformation on social media platforms relevant to COVID-19. To facilitate our work, we have utilized the dataset of "CONSTRAINT shared task 2021". Our research has shown that language and ensemble models are well adapted to this role, with a 97% accuracy.