Combating the infodemic: COVID-19 induced fake news recognition in social media networks

• DOI: 10.1007/s40747-022-00672-2
• Publication Date: 2022-02-18T09:04:58Z
• Authors: Shankar Biradar; Sunil Saumya; Arun Chauhan
• Keywords: Misinformation; Identification

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