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Author: search.filters.author.Boongasame, LaorSubject: search.filters.subject.COVID-19

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    COVID-19 Fake News Detection with Deep Learning
    (2023-01-01)
    Kowirat, Rutchaneewan
    ;
    Boongasame, Laor
    Social media has become one of the most popular channels to keep updated with daily news because it can quickly and easily access information. This advantage is used by malicious people to spread fake news widely. Since the COVID-19 pandemic, fake news has become a huge social problem, causing people to panic and misunderstand how to cure or protect themselves from the virus. So, the goal of this research is to use deep learning as the Recurrent Neural Network (RNN) model to find fake news about COVID-19 in the Thai language on social media and help filter information by classifying real and fake news.
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    Fake News Detection on Social Media: Case Study of 2019 Novel Coronavirus
    (2021-12-17)
    Kowirat, Rutchaneewan
    ;
    Boongasame, Laor
    Fake news is news that is created with the intent to deceive and mislead readers. It is a problem that occurs in every era because it creates misunderstandings for people through a variety of media channels such as newspapers, radio, or television. Nowadays, fake news has become a big problem. When social media has become another channel to increase the spread of fake news and came to play a big role during the epidemic like COVID-19. Fake news creates panic and creates false knowledge of how to protect yourself from COVID-19. Therefore, the objective of this research is to create a method that can detect fake news on social media. It focuses only on news related to COVID-19. In addition, the information was extracted directly from social media such as Twitter. Moreover, this research applying machine learning processes to classify real and fake news. From the experimental results, the accuracy was measured at 99.92% with the Decision Tree model.