Datasets and Approaches of COVID-19 Misinformation Detection: A Survey

ABSTRACT

Social media has become a primary source of news, providing a fertile environment for spreading misinformation. Since the outbreak of the COVID-19 pandemic, misleading information related to COVID-19 has been spreading rapidly and widely on social media. Several conspiracy theories have emerged regarding the origin of the COVID-19, potential treatments, and vaccines posing a real threat to the public health of people. Fake news that promotes vaccine hesitancy might jeopardize achieving the levels of vaccination needed to reach herd immunity and end the pandemic. The need for automatic tools that detect COVID-19 related misinformation has encouraged researchers to propose several Machine learning (ML) and Deep Learning (DL). Many datasets have been released since the start of the pandemic, aiming to assess the performance of misinformation detection methods. This survey reviews the datasets that have been released to analyze the related to COVID-19 in general and COVID-19 misinformation detection in particular released in Arabic, English, and other languages. We also provide an overview of the different methods used to detect COVID-19 fake news. In this paper, the terms 'misleading information', 'misinformation', and 'fake news' are used interchangeably. © 2022 IEEE.