Detection of False Information Regarding Covid-19. A study
Keywords:
Fake News Detection, Information Security, Social MediaAbstract
The surge in the dissemination of false information on social media platforms, particularly regarding Covid-19, poses a significant threat to both the mental and physical well-being of individuals. Detecting and preventing the spread of such misinformation is a crucial undertaking. This article provides an overview of various approaches employed for the detection of fake news related to Covid-19, encompassing Classical Machine Learning models, Neural Network-based models, and those derived from alternative methodologies and preprocessing steps. The analysis includes insights from the "Constraint@AAAI2021 - COVID19 Fake News Detection" challenge, which aimed to binary classify news sourced from social media into fake and real categories. We examine the most effective approaches proposed by researchers during the challenge. Additionally, we detail datasets containing Covid-19-related fake news, offering valuable resources for the detection and classification of such misinformation.
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