Abstract
Fake news are widely offered in digital media to raise the visitors hit and in an offbeat, it acts on users emotions. The foremost ordinary example of such fake news throughout this pandemic, are the various remedies to cure covid. As a result of which individuals are unable to acknowledge any kind of genuine news. People try and attempt numerous things which will never help in curing this contagious disease. Moreover, it might lead to some other major health issues. In this paper, a framework is provided for the classification of news as fake vs real. Text data is pre-processed using Natural Language Processing (NLP) by performing tokenization, text cleaning and vectorization. N-gram and TF-IDF vectorization is used. Seven Machine Learning (ML) algorithms are then applied for classification. Two different datasets Kaggle and ISOT is used for experimentation and evaluated on the same scale using different evaluation metrics to demonstrate the efficacy of the proposed framework.
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Dubey, Y., Wankhede, P., Borkar, A., Borkar, T., Palsodkar, P. (2022). Framework for Fake News Classification Using Vectorization and Machine Learning. In: Lahby, M., Pathan, AS.K., Maleh, Y., Yafooz, W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-90087-8_16
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