Abstract
Fake news is a form of news consisting of false statements from the real ones spread via news media or online social media. In this paper, we aim for the fake news detection model which is capable of detecting the fake news from large amounts of data that are daily produced on online platforms. The approach for our model is a machine learning technique which is text analysis and for classifying fake news we have used k means clustering. Using the data preprocessing, classification, and topic modeling we get topics from the article, and they are compared with legitimate news. We modeled a framework named Fake News Detection (FND) which is used to classify the news articles. By streaming detection of fake information, we can control false or inaccurate content.
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Amanchi, U.M., Badam, N., Elaganti, R.L. (2021). Fake News Detection Using Text Analytics. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-0878-0_12
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DOI: https://doi.org/10.1007/978-981-16-0878-0_12
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