Abstract
People can get infected with fake news very quickly with misleading words and images and post them without any fact-checking. The social media life has been used to distribute counterfeit data, which has a significant negative influence on individual consumers and on a wider community. The fake news problem is tackled using a machine learning algorithm. Different classifiers are used for the purpose of identifying fake news. In this paper, Passive-Aggressive Classifier is implemented for this purpose. The approach is implemented on two datasets of fake and real news. After performing the experiment, it is observed that Passive-Aggressive Classifier provides an accuracy of 97.5%. The performance of the proposed model is compared with the existing methods. The Passive-Aggressive Classifier provides the best result compared to others.
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Gupta, S., Meel, P. (2021). Fake News Detection Using Passive-Aggressive Classifier. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_13
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DOI: https://doi.org/10.1007/978-981-15-7345-3_13
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