Detection of Fake News Using Machine Learning



news, fake news detection, machine learning, passive aggressive classifier


For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news.


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How to Cite

M. S. Rawat, A. . Srivastava, and S. . Aggarwal, “Detection of Fake News Using Machine Learning”, Int J Eng and Appl Phys, vol. 1, no. 2, pp. 205–209, May 2021.