Abstract
Fake news and Bias news play a very important role in spreading misinformation and thereby manipulating people’s perceptions to distort their awareness and decision-making. In the proposed model for finding the bias, sentiment analysis has been used and for fake news classification, the passive-aggressive classifier has been used. The accuracy achieved for fake news classification using passive aggressive is 95.9%. The bias of an article is calculated based on the sentiment score and the score assigned to the author and publisher based on their average bias score.
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Ghadekar, P., Tilokchandani, M., Jevrani, A., Dumpala, S., Dass, S., Shinde, N. (2021). Prediction and Classification of Biased and Fake News Using NLP and Machine Learning Models. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_2
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DOI: https://doi.org/10.1007/978-981-33-4859-2_2
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