Abstract
Fake news is a severe problem for society, as seen by its increasing severity in recent years. Fake news poses a threat on social, political, economic, and psychological levels, all of which have an impact on one’s personality. In this study, we compare linear classifiers in machine learning to see which ones are the most effective at detecting false news. The proposed work is recognized the fake news and distinguishes it with the least amount of work and time, as well as the highest accuracy. Machine learning methods were combined with natural language processing in the suggested system. The suggested approach was tested on a standard data set of news stories categorized as false and true by reliable news sources, and the results concluded that the best linear classifiers in this field are the ones that use the linear regression algorithm.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ateaa, H.Y., Hasan, A.H., Ali, A.S. (2023). Detection of Fake News by Machine Learning with Linear Classification Algorithms: A Comparative Study. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_68
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DOI: https://doi.org/10.1007/978-981-19-7615-5_68
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