Abstract
Everyone in the modern era, where the internet is widely used, relies on a range of online sources for news channels. Because more people are using Facebook, Instagram, and other social media platforms, news has spread swiftly to crores of people in a short amount of time. The spread of false information has far-reaching effects, such as influencing election results in favor of particular politicians or fostering prejudiced opinions. Additionally, spammers use captivating news headlines to generate revenue via clickbait advertisements. This study uses machine learning (ML) approaches to categorize a variety of online news items using natural language, artificial intelligence, processing, and ML techniques. With the help of this study, consumers will be able to categorize news as either true or fraudulent and confirm the reliability of the website that originally published it. It uses a variety of techniques, including Naive Bayes and Decision Tree classifiers, to categorize the news as phony or true.
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Pavitha, N., Dargode, A., Jaisinghani, A., Deshmukh, J., Jadhav, M., Nimbalkar, A. (2024). Fake News Detection Using Machine Learning. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_40
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DOI: https://doi.org/10.1007/978-981-99-7954-7_40
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