FAKE NEWS DETECTION ABOUT SARS-COV-2 PANDEMIC USING NEUTRAL NETWORKS AND DETECTION ALGORITHMS
FAKE NEWS DETECTION ABOUT SARS-COV-2 PANDEMIC USING NEUTRAL NETWORKS AND DETECTION ALGORITHMS

Summary/Abstract: Fake news has an extremely high impact on society, spreading quite simple and fast through social media, TV, internet, press, and other means of communication. The false news about the new coronavirus is blocked by the authorities, according to the decree for establishing a state of emergency. The misinformation of the population and the placement of fake news is two inevitable consequences in times of crisis, these being amplified by two other elements, which feed each other: fear of illness, which can cause deaths, but also uncertainty or lack of information on how to manage the crisis and what is involved. The need to stop the spreading of fake news, it's paramount and this paper proposes to recognizing truthful information from false information during the pandemic COVID-19 through a guide learning method. This guide implies a model for distinguishing false messages in the online environment, such as Machine Learning algorithms, which can have an accuracy of over 95%.

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